we’re residing in a digital generation. In every phase of our lifestyles and daily wishes, we’ve a widespread use of technology. with out technology, the sector will genuinely stand nonetheless. have you ever ever attempted to discover what it takes to prepare a simple weather forecast? lots of facts are analyzed to prepare simple statistics, that’s brought to us in actual time. computer systems are the most crucial find of the technology revolution and they have modified the world substantially within the last few decades. computers process those huge units of records and helps us in each era-established assignment and want. that allows you to make pc operation efficient, we represent data in special formats or we can name in distinctive structures, that are called records structures.
information systems are very essential additives for computers and programming languages. along side statistics systems, it is also very important to know the way to solve a hassle or find a answer the use of these statistics systems. From our easy cell telephone touch ebook to complicated DNA profile matching systems, using records systems and algorithms is anywhere.
Have we ever notion that status in a superstore queue to payout may be a illustration of facts shape? Or getting rid of a invoice from a pile of papers may be some other use of information shape? In truth, we’re following data shape standards nearly anywhere in our lives. whether we’re dealing with the queue to pay the bill or to get to the transportation, or retaining a stack for a pile of books or papers for day by day works, information structures are anywhere and impacting our lives.
personal home page is a completely popular scripting language and billions of web sites and applications are constructed the usage of it. human beings use Hypertext Preprocessor (Hypertext Preprocessor) for simple applications to very complex ones and a few are very information extensive. The big query is–have to we use Hypertext Preprocessor for any data in depth application or algorithmic answers? Of direction we have to. With the brand new launch of php 7, personal home page has entered into new possibilities of green and robust utility improvement. Our undertaking can be to show and prepare ourselves to recognize the strength of records systems and algorithms using personal home page 7, so that we are able to put it to use in our programs and packages.

If we take into account our actual-existence state of affairs with computers, we also use different types of preparations of our property and facts in order that we can use them successfully or discover them without difficulty when wanted. What if we input our smartphone contact book in a random order? will we be capable of find a touch effortlessly? we’d emerge as searching every and every contact within the book as the contacts aren’t arranged in a selected order. just don’t forget the following photographs:

One suggests that the books are scattered and finding a specific e book will take time as the books are not prepared. the opposite one suggests that the books are prepared in a stack. now not simplest does the second image display that we’re the usage of the gap neatly, however additionally the searching of books will become less complicated.

allow us to take into account any other example. we’re going to buy tickets for an essential soccer in shape. There are lots of people looking forward to the ticket booth to open. Tickets are going to be dispensed on a primary come first served foundation. If we remember the following photographs, which one is the first-class way of managing such a massive crowd?:

The left image definitely indicates that there’s no right order and there’s no manner to realize who came first to get the tickets. but if we knew that humans have been waiting in a based way, in a line, or queue, then it’ll be less difficult to deal with the gang and we are able to surrender the tickets to whoever got here first. this is a common phenomenon called a queue which is closely used within the programming world. Programming phrases aren’t generated from outdoor the world. In reality, most of the people of the records systems are stimulated from real lifestyles and they use the identical terms most of the times. whether or not we are preparing our assignment list, touch listing, book piles, weight loss program charting, getting ready a family tree, or organization hierarchy, we’re essentially the use of distinct association techniques which can be referred to as statistics structures inside the computing international.

we’ve got talked a touch about information systems so far but what approximately algorithms? don’t we use any algorithms in our day by day lives? really we do. whenever we are searching for a contact from our antique smartphone e book, we are truely now not looking from the start. If we’re looking for Tom, we are able to now not search the page in which it says A, B, or C. we’re at once going to the web page T and could locate if Tom is listed there or now not. Or, if we want to discover a physician from a telephone listing, we are able to truely now not seek within the foods segment. If we don’t forget the cellphone e-book or telephone directory as statistics structures, then the way we look for unique data is known as algorithms. even as information structures help us to apply facts efficiently, algorithms help us to perform special operations on the ones records successfully. for instance, if we have a hundred,000 entries in our cellphone listing, looking a specific access from the start may take a long term. however, if we recognize the doctors are listed from page 2 hundred to 220, we can seek best the ones pages to save our time by means of looking a small phase rather than the full directory:

PALO ALTO, Calif.–(BUSINESS WIRE)–Data Theorem, Inc., a leading provider of modern application security, today announced that its Cloud Secure cloud-native security solution has won the 2021 Fortress Cyber Security Award. The industry awards program identifies and rewards the world’s leading companies and products that are working to keep our data and electronic assets safe among a growing threat from hackers.

Cloud Secure is the industry’s first solution delivering attack surface management security for cloud-native applications that starts at the client layer (mobile and web), protects the network layer (REST and GraphQL APIs), and extends down through the underlying infrastructure (cloud services). Its combination of attack surface management and defensive protections enables both offensive and defensive security capabilities to best prevent data breaches of cloud-native applications and serverless cloud functions.

“We are so proud to name Data Theorem’s Cloud Secure as a winner in the 2021 Fortress Cyber Security Awards program,” said Maria Jimenez, Chief Nominations Officer, Business Intelligence Group. “As our society continues to evolve and become more reliant on networks and data, companies such as Data Theorem are critical at providing the protection and trust consumers demand.”

For information about the annual Fortress Cyber Security Awards, please visit https://www.bintelligence.com/fortress-cyber-security-awards.

“Looking at Data Theorem’s long list of analyst and industry award honors, it is particularly gratifying to be recognized in programs such as Business Intelligence Group’s Fortress Cyber Security Awards program, where winners are determined by business executives that have experience and knowledge in the industry,” said Doug Dooley, Data Theorem COO. “Cloud Secure shined against the competition in the program’s unique scoring system for its unique innovations securing public cloud environments and the enterprise applications that depend on those cloud services. Organizations turn to Data Theorem to protect their cloud applications due to the benefits of our dynamic and run-time analysis with offensive attack surface management and defensive protection toolkits. This enables organizations to conduct continuous, automated security inspection and remediation of their most important cloud-native applications.”

Data Theorem’s broad AppSec portfolio protects organizations from data breaches with application security testing and protection for modern web frameworks, API-driven microservices and cloud resources. Its solutions are powered by its award-winning Analyzer Engine, which leverages a new type of dynamic and run-time analysis that is fully integrated into the CI/CD process, and enables organizations to conduct continuous, automated security inspection and remediation.

Business Intelligence Group
The Business Intelligence Group was founded with the mission of recognizing true talent and superior performance in the business world. Unlike other industry award programs, these programs are judged by business executives having experience and knowledge. The organization’s proprietary and unique scoring system selectively measures performance across multiple business domains and rewards those companies whose achievements stand above those of their peers.

Data Theorem
Data Theorem is a leading provider of modern application security. Its core mission is to analyze and secure any modern application anytime, anywhere. The award-winning Data Theorem Analyzer Engine continuously analyzes APIs, Web, Mobile, and Cloud applications in search of security flaws and data privacy gaps. Data Theorem products help organizations prevent AppSec data breaches. The company has detected more than 1 billion application eavesdropping incidents and currently secures more than 8,000 modern applications for its enterprise customers around the world. Data Theorem is headquartered in Palo Alto, Calif., with offices in New York and Paris.

PALO ALTO, Calif.–(BUSINESS WIRE)–Data Theorem, Inc., a leading provider of modern application security, today announced that its Web Secure AppSec solution has earned the top Hot Security Technology of the Year Award from the Globee® Awards, organizers of the world’s premier business awards programs and business ranking lists. Data Theorem earned the Gold Award in the Security Software awards category of the 17th Annual 2021 Cyber Security Global Excellence Awards® last week

These prestigious global awards recognize cyber security and information technology vendors with advanced, ground-breaking products, solutions, and services that are helping set the bar higher for others in all areas of security and technologies. More than 45 judges from around the world representing a wide spectrum of industry experts participated in the judging process.

Web Secure is the industry’s first full-stack application security analyzer that provides vulnerability analysis for modern web applications from the web-layer down to its embedded APIs and cloud resources. This product enables DevOps and security teams to improve web application security testing for issues that have plagued the industry for years by both identifying and helping remediate potential data breaches in modern web applications, also known as Single-Page Applications (SPAs).

“The industry awards continue to roll in this year for Web Secure, this time being recognized by the Globee Awards as the hottest security technology of the year for its unique design to help customers secure their modern web applications,” said Doug Dooley, Data Theorem COO. “Web Secure uniquely leverages a new type of dynamic and run-time analysis that is fully integrated into the CI/CD process, and enables organizations to conduct continuous, automated security inspection and remediation of their modern web applications.”

Data Theorem’s broad AppSec portfolio protects organizations from data breaches with application security testing and protection for modern web frameworks, API-driven microservices and cloud resources. Its solutions are powered by its award-winning Analyzer Engine, which leverages a new type of dynamic and run-time analysis that is fully integrated into the CI/CD process, and enables organizations to conduct continuous, automated security inspection and remediation. Data Theorem’s AppSec portfolio secures mobile apps, cloud-driven APIs, and modern web applications.

Globee Awards

Globee Awards are conferred in eleven programs and competitions: the CEO World Awards®, the Consumer World Awards®, The Customer Sales & Service World Awards®, the Globee® International Best in Business Awards, the Golden Bridge Awards®, the Cyber Security Global Excellence Awards® and Security World Awards, the IT World Awards®, One Planet® American Best in Business Awards, the Globee® Employer Excellence Awards®, the Globee® Corporate Communications & Marketing World Awards, and the Women World Awards®. Honoring organizations of all types and sizes and the people behind their success, the Globee Awards recognize outstanding achievements and performances in businesses worldwide.

Data Theorem

Data Theorem is a leading provider of modern application security. Its core mission is to analyze and secure any modern application anytime, anywhere. The award-winning Data Theorem Analyzer Engine continuously analyzes APIs, Web, Mobile, and Cloud applications in search of security flaws and data privacy gaps. Data Theorem products help organizations prevent AppSec data breaches. The company has detected more than 1 billion application eavesdropping incidents and currently secures more than 8,000 modern applications for its enterprise customers around the world. Data Theorem is headquartered in Palo Alto, Calif., with offices in New York and Paris

As demand for digital services skyrockets, the three largest public cloud vendors in the world have effectively taken over the hyperscale data center market, according to new data from Synergy Research Group.

Amazon, Microsoft and Google collectively now account for more than 50 percent of the world’s largest data centers across the globe as the three companies continue to spend billions each year on building and expanding their global data center footprint to accommodate the high demand for cloud services. The global COVID-19 pandemic spurred a record-breaking third quarter 2020 as data center spending reached $37 billion, led by AWS, Google and Microsoft.

The number of large data centers operated by hyperscale providers like AWS, Microsoft and Google increased to nearly 600 by the end of 2020, twice as many as there were in 2015. Amazon and Google opened the most new data centers in the past 12 months, accounting for half of the total amount of new hyperscale data centers opened in 2020.
There were 111 new hyperscale data centers opened in the last eight quarters, with 52 of those coming onstream in 2020 despite COVID-19 causing a few logistical issues,” said John Dinsdale, a Chief Analyst at Synergy Research Group in a statement. “That is testament to the ongoing robust growth in the digital services that are driving those investments – particularly cloud computing, SaaS, e-commerce, gaming and video services.”
The global COVID-19 pandemic has significantly shifted how businesses and organizations of all sizes buy and use online tools, from video-conferencing and virtual business events to entertainment and remote education. This change was enabled by cloud environments, particularly public clouds.
Businesses are gobbling up server, storage and networking products for their public cloud infrastructure at a rapid pace.

Spending on public cloud infrastructure reached a massive $13.3 billion in the third quarter of 2020 alone, representing an increase of 13 percent year over year, according to IT research firm IDC. Dell Technologies, Hewlett Packard Enterprise and Inspur are leading the market in terms of cloud infrastructure sales as the companies lead the server, storage and hyperconverged infrastructure markets.

“In addition to almost 600 operational data centers, we have visibility of a further 219 that are at various stages of planning or building, which is good news indeed for data center hardware vendors and wholesale data center operators,” said Synergy’s Dinsdale.

Compared to traditional data centers, hyperscale data centers contain much more IT products, typically housing tens of thousands of servers and hardware alongside millions of virtual machines. Hyperscale data centers focus on infrastructure optimization and scale acceleration in their designs and how products are powered and managed.

It is key to note that companies like AWS, Google and Microsoft don’t build every single data center by themselves, typically leasing capacity and space from data center operations like Digital Realty and Equinix. Synergy said that approximately 70 percent of all hyperscale data centers are located in facilities that are leased from data center operators or are owned by partners of the hyperscale operators.

In terms of geography, the U.S. accounts for almost 40 percent of the major cloud and internet data center sites. The next most popular regions are China, Japan, Germany, the U.K. and Australia, which collectively account for another 29 percent of the total, according to Synergy.

2019 saw more data organizations running advanced analytics, AI, and machine learning workloads than ever before. Heading into 2020, the expectation was a spike in both the number of technologies and the data teams required to support those workloads internally. AI and analytics teams were predicted to merge into unified data organizations, tackling challenges such as hybrid cloud strategy, Kubernetes-based analytics, and the ongoing evolution of the Hadoop ecosystem.

1. The Rise of the Hybrid Cloud

After years of discussion, hybrid cloud was expected to move from concept to practical reality in 2020. Large enterprises were already refusing to expand on-premises Hadoop deployments, choosing instead to invest in public cloud capacity. However, most were unwilling to migrate core enterprise data off-premises entirely.

The predicted model: data stays on-prem, compute bursts to the cloud. This approach targets peak demand and unpredictable workloads, where elastic cloud compute is more cost-effective than maintaining excess on-premises capacity. Technologies enabling seamless data-local-compute-remote architectures were expected to lead the hybrid cloud wave.

2. One Machine Learning Framework to Rule Them All

By late 2019, operationalizing ML model training had become a priority across organizations of all sizes. Multiple frameworks competed for dominance, but no clear winner had emerged. The analogy drawn was to earlier data tooling consolidations: Apache Spark became the standard for data transformation, and Presto was emerging as the leading choice for interactive querying.

The prediction for 2020 was that the model training space would see similar consolidation, with PyTorch and TensorFlow identified as the leading contenders for the dominant framework position.

3. Kubernetifying the Analytics Stack

Containers and Kubernetes had proven highly effective for stateless workloads — web servers, microservices, self-contained databases — but adoption for advanced analytics and AI workloads lagged behind. The prediction was that 2020 would mark the shift toward AI and analytics workloads becoming mainstream on Kubernetes.

The core technical challenge: solving data sharing and elasticity. Moving data from remote silos into Kubernetes clusters improves data locality, which is critical for performance-sensitive analytics jobs. This required new patterns for stateful workload management that the community was actively developing.

4. HDFS Is Dead — But Hadoop Compute Lives On

The “Hadoop is dead” narrative was common heading into 2020, but the reality was more nuanced. The prediction separated two distinct components of the Hadoop ecosystem:

• HDFS (Hadoop Distributed File System): Predicted to decline due to operational complexity, high cost, and a fundamental inability to support elastic compute scaling. Data stored in HDFS would migrate to cloud object storage (such as S3 or GCS) or on-premises object storage alternatives.
• Hadoop compute frameworks (Spark, Presto): Predicted to remain strong. These tools had already been adopted well beyond the Hadoop ecosystem and were seen as foundational for large-scale data processing regardless of the underlying storage layer.

The key insight: compute and storage decoupling was accelerating, making storage-layer-agnostic compute frameworks more valuable, not less.

5. AI and Analytics Teams Merge Into a Unified Data Organization

The organizational structure of data teams was also expected to evolve. Hadoop platform teams had already transitioned into analytics and data engineering teams. The next step was absorbing AI and ML functions into the same organizational unit, reflecting how deeply intertwined structured data analytics and machine learning had become.

The convergence of statistical modeling and computer science into modern ML meant that data, analytics, and AI teams were often working on the same datasets with overlapping goals. The predicted outcome: organizations would build dedicated, unified data stack teams responsible for storage, compute, and modeling across on-prem and cloud environments.

6. The Talent Gap Will Constrain Data Technology Adoption

A persistent challenge identified for 2020 was the shortage of engineers who could bridge data engineering, cloud architecture, and AI/ML workloads. Building production data stacks that incorporate modern cloud, Kubernetes, and ML tooling requires a rare combination of skills.

The concern: as executive-level interest in data-driven strategies grew, the pipeline of engineers capable of architecting those systems was not keeping pace. Companies making ambitious “data-everywhere” commitments were expected to fall short without a parallel investment in developing or recruiting the necessary talent.

7. China’s Cloud Adoption Leapfrog

While U.S. enterprises had been migrating to public cloud infrastructure for several years, Chinese enterprises had largely invested in on-premises infrastructure for data-intensive workloads. The prediction for 2020 was that this would change dramatically — with Chinese enterprises leapfrogging incremental cloud adoption to deploy advanced workloads directly in the public cloud at significant scale.

Public cloud providers operating in China were expected to see growth rates potentially outpacing those of established Western cloud giants, driven by new use cases, burst compute for peak loads, and migration of existing on-prem workloads.

Key Takeaways for Data Engineering Teams

• Plan for hybrid architectures: Separating data residency from compute elasticity is a practical near-term strategy, not a distant goal.
• Watch the ML framework landscape: Framework consolidation simplifies toolchain decisions; betting on widely-adopted frameworks reduces long-term migration risk.
• Invest in Kubernetes expertise for analytics: Stateful analytics workloads on Kubernetes require different patterns than stateless services — build that knowledge proactively.
• Decouple storage from compute: Moving away from HDFS toward object storage unlocks the elasticity needed for modern cloud-native analytics.
• Build unified data teams: Siloed analytics and AI functions create duplication; unified teams sharing infrastructure and data assets are more efficient.
• Address the talent gap directly: Upskilling existing engineers in cloud-native and ML tooling is often faster than competing for a limited pool of specialists.

The trends outlined here represent the data engineering inflection points anticipated heading into 2020 — a year when advanced analytics and AI were expected to move from experimental to operational across a much wider range of organizations.

This article breaks down the most significant recent additions to the AWS machine learning platform, organized by audience: infrastructure-focused developers and DevOps engineers who build and deploy custom models, and data scientists or non-specialists who need managed services to analyze large datasets without hand-rolling ML pipelines.

Who AWS Is Building For

AWS machine learning investments fall into two broad categories:

• Developer and DevOps tooling — Deep Learning AMIs and Deep Learning Containers target engineers building sophisticated, custom models and the teams responsible for deploying them on cloud infrastructure.
• Managed AI services — Services like Amazon Forecast and Amazon Personalize are designed for data scientists and non-specialists who want to apply advanced ML techniques to large datasets without managing the underlying infrastructure or writing training code from scratch.

AWS Deep Learning Containers

Setting up a reproducible environment for deep learning development has historically been a time-consuming task. Data scientists had to manually resolve dependencies across multiple source repositories, manage framework versions, and tune configuration for their target hardware — a largely DIY process prone to environment drift and hard-to-debug failures.

NVIDIA addressed this problem early with NVIDIA GPU Cloud (NGC), a registry of pre-built containers that bundle all the libraries and drivers needed to run deep learning workloads on NVIDIA hardware. AWS adopted a similar approach and introduced AWS Deep Learning Containers: a set of Docker images pre-configured with the most popular deep learning frameworks.

Supported Environments

AWS Deep Learning Containers can run across a range of AWS compute services and orchestration platforms:

• Amazon Elastic Kubernetes Service (EKS)
• Amazon Elastic Container Service (ECS)
• Amazon SageMaker
• Individual EC2 instances
• Self-managed Kubernetes clusters backed by EC2 instances

Frameworks and Runtime Support

At the time of this writing, the containers shipped with support for TensorFlow 1.14.0, MXNet 1.4.1, and the NVIDIA CUDA toolkit 10.0. Both frameworks expose Python APIs, which is the dominant scripting language in the data science community.

Key capabilities include:

• Model training and inference with both TensorFlow and MXNet.
• Distributed training with TensorFlow, supported via Horovod — an open-source distributed training framework originally developed by Uber.
• CPU and GPU instance compatibility on Ubuntu 16.04.
• SageMaker integration — useful for teams that want to run custom algorithms and containers without managing the training infrastructure directly.

Availability and Deployment

Deep Learning Containers are available at no additional charge from the AWS Marketplace or Amazon Elastic Container Registry (ECR). You pay only for the underlying compute resources you consume.

For Kubernetes-based deployments, the workflow is straightforward: create a pod specification file that references one of the Deep Learning Container images, then schedule it onto your cluster using kubectl. The same container images can be used consistently across local development, EKS, and SageMaker, reducing environment parity issues between development and production.

Deep Learning AMIs: Updates for Smaller-Scale Projects

For teams working on smaller projects — those involving a handful of VMs rather than a full container orchestration cluster — AWS maintains Deep Learning Amazon Machine Images (AMIs). These provide a similar pre-configured environment directly on EC2 instances, without the need to manage container infrastructure.

In 2019, AWS expanded framework support for Deep Learning AMIs with the following additions:

• PyTorch 1.1, Chainer 5.4, and CUDA 10 support for MXNet
• Amazon Linux 2 support, along with TensorFlow 1.13.1, MXNet 1.4.0, and Chainer 5.3.0

AWS also extended the Elastic Inference service to support TensorFlow 1.12, adding a new EIPredictor Python API. Elastic Inference allows you to attach a fractional GPU accelerator to any SageMaker or EC2 instance — rather than provisioning a full GPU instance — which can significantly reduce the cost of deep learning inference workloads while still improving throughput over CPU-only instances.

AWS AI Managed Services: Now Generally Available

Several AI services previewed at re:Invent 2018 reached general availability in the months that followed. These managed services abstract away model training, infrastructure provisioning, and hyperparameter tuning — users supply data, and AWS handles the ML pipeline.

Amazon Forecast

Amazon Forecast is a fully managed time series forecasting service built on technology Amazon developed for its own internal demand-planning use cases. Users supply historical time series data and any relevant metadata, and Forecast automatically selects and trains a predictive model. The service is designed to handle the kind of complex, multi-variable forecasting problems — such as product demand, resource utilization, or financial metrics — that are difficult to address with simple statistical methods.

Amazon Personalize

Amazon Personalize is a managed recommendation and personalization engine that draws on the same underlying models used in Amazon’s retail platform. Teams provide an interaction dataset — such as page views, purchases, or content engagement events — along with an item catalog. Personalize handles model training, tuning, and hosting, and returns ranked recommendations via an API. This makes it practical to add real-time personalization to applications without dedicated ML engineering resources.

Amazon ML Insights for QuickSight

ML Insights is an add-on module for Amazon QuickSight, AWS’s interactive business intelligence service. It introduces machine learning and natural language processing features directly into the BI workflow, including:

• Anomaly detection — automatically surfaces unexpected changes in metrics across large datasets.
• Forecasting — projects future values of time series data within QuickSight dashboards.
• Auto-narratives — generates plain-language textual descriptions of chart data, making it easier to communicate findings to non-technical stakeholders.

ML Insights is aimed at analysts who need more than standard statistical aggregation but do not want to build and maintain separate ML pipelines outside of their BI tooling.

Key Takeaways for DevOps and ML Teams

• If your team builds custom deep learning models, Deep Learning Containers remove the environment setup burden and work across EKS, ECS, SageMaker, and raw EC2.
• For smaller VM-based workloads, Deep Learning AMIs offer a faster path to a pre-configured training environment without container orchestration overhead.
• Elastic Inference is worth evaluating if inference cost on GPU instances is a concern — attaching fractional accelerators can reduce spend compared to full GPU instances.
• Managed services like Forecast and Personalize are production-ready options for common ML use cases where building and maintaining custom models is not justified by the available ML engineering capacity.

Source: Originally based on reporting from searchaws.techtarget.com (2019).

The Scale Problem No Dashboard Was Built For

Four billion is a number that challenges human intuition. Now picture four billion Kubernetes container instances — each one generating telemetry data on spin-up, runtime behavior, and spin-down — every single week. According to Nancy Gohring, senior analyst at 451 Research LLC, that is approximately the scale at which Google runs Kubernetes for its own internal workloads.

“If you think of Google launching 4 billion containers every week, they’re collecting a number of different data points,” Gohring said. “It’s not even just four billion pieces of data. The volume of operations data that people are faced with is just out of this world.”

Google is an extreme example, but the underlying trend applies broadly. Any organization running microservices at meaningful scale faces an exponential growth in operational signals: logs, metrics, traces, events, and alerts multiplying with every new service, replica, and deployment pipeline.

Autonomous DevOps Teams and the Oversight Gap

The shift to distributed, autonomous DevOps teams was designed to increase velocity. Small, self-sufficient squads own their services end-to-end — from writing code to running it in production. This model works well for speed, but it introduces a subtle organizational risk: no one may be watching the whole application.

“DevOps are running autonomously; they’re doing their own thing so they can move quickly,” Gohring noted. “But is there anybody overseeing the application that’s made up of maybe a thousand microservices? In some cases, the answer is no.”

When an application is composed of hundreds or thousands of loosely coupled services, each owned by a different team, systemic failures — cascading latency, inter-service dependency issues, degraded user experience — can go undetected until they become customer-impacting incidents. Individual team dashboards only show local health; end-to-end application health requires a broader view.

Tool Sprawl: When Every Team Chooses Its Own Monitoring Stack

The natural response to rising operational complexity is to add monitoring tooling. But in organizations where teams have full autonomy, this often results in a fragmented landscape where each team adopts whichever tool fits its immediate needs — Prometheus for one team, Datadog for another, a bespoke ELK stack for a third.

“As applications are getting much more complex, looking at that data in a siloed tool tends to obscure the bigger picture,” Gohring said. “In most cases, businesses aren’t going to just settle on one monitoring tool that does it all. If you have 100 DevOps teams and they have chosen their own monitoring tools, this is not efficient.”

The practical consequences of monitoring tool sprawl include:

• Correlation blind spots: When logs, metrics, and traces live in separate systems, correlating an anomaly in one service with a root cause in another becomes a manual, time-consuming process.
• Duplicated licensing and operational costs: Maintaining multiple monitoring platforms multiplies vendor contracts, training overhead, and integration work.
• Inconsistent alerting standards: Teams using different tools will configure thresholds, severities, and on-call workflows differently, leading to inconsistent incident response.
• Onboarding friction: Engineers rotating between teams or responding to cross-team incidents must context-switch between unfamiliar dashboards under pressure.

The Push Toward Autonomous and Unified Monitoring

The data volume problem is large enough that human operators alone cannot triage it in real time. This is pushing vendors and platform teams toward two complementary approaches:

1. Autonomous/AI-assisted monitoring: Systems that automatically detect anomalies, suppress noise, and surface only the signals that require human attention — reducing alert fatigue and speeding mean time to detect (MTTD).
2. Unified observability platforms: Consolidating logs, metrics, and traces into a single platform gives teams a shared language for application health and enables cross-team correlation without requiring everyone to adopt the same instrumentation library.

Neither approach eliminates the need for skilled engineers, but both aim to make the signal-to-noise ratio manageable at cloud-native scale.

Practical Takeaways for DevOps and Platform Teams

If your organization is feeling the strain of monitoring tool sprawl or drowning in operational telemetry, consider the following steps:

• Audit your current tooling landscape. Map which teams use which monitoring tools and identify where correlation gaps exist between services.
• Establish organization-wide observability standards. Define minimum requirements for logging format, metric naming conventions, and distributed tracing instrumentation — without mandating a single tool everywhere.
• Designate application-level ownership. Beyond service-level ownership, assign responsibility for monitoring the health of entire user-facing application flows, not just individual microservices.
• Evaluate consolidation incrementally. Full platform consolidation is disruptive. Start by federating data into a central store for querying and alerting, while allowing teams to retain their preferred instrumentation.
• Build runbooks that span services. Incident response documentation should reflect the reality that failures rarely respect service boundaries.

The Bigger Picture

The operations data avalanche is not a temporary growing pain — it is a structural feature of cloud-native architectures. As container density, deployment frequency, and microservice counts continue to rise, the gap between the data generated and a team’s capacity to interpret it will only widen. Organizations that address monitoring fragmentation proactively will be better positioned to maintain reliability and developer productivity at scale.

Source interview originally published by SiliconANGLE / theCUBE, recorded at the Sumo Logic Illuminate event.

According to an OpenStack user survey conducted in 2018, 61% of OpenStack deployments were also running Kubernetes alongside it. Whether or not that figure has shifted since, the directional trend is clear from industry activity alone.

Industry Signals: Why Kubernetes and OpenStack Are Converging

Several high-profile initiatives illustrate how seriously the industry has taken this combination:

• AT&T and Mirantis entered a multi-year agreement to develop a 5G core infrastructure backed by both Kubernetes and OpenStack.
• Platform9 launched a managed offering bundling OpenStack and Kubernetes into a single solution stack for service providers and developers, including support for Kubernetes on VMware.
• Nokia’s CloudBand platform incorporated both Kubernetes and OpenStack for workload orchestration in telco environments.
• The OpenStack Foundation’s Airship project was announced specifically to bring OpenStack and Kubernetes together into a unified lifecycle management framework for data centres.

The Shift from VNFs to CNFs

The core of a modern telecom network — and increasingly the virtualised core of enterprise data centres — has undergone a significant architectural shift. Network functions moved from dedicated physical hardware to Virtual Network Functions (VNFs) running in virtual machines. The next step in this evolution is Cloud-Native Network Functions (CNFs), which run in containers and embrace CI/CD-driven development and deployment patterns.

This progression was described in a Telecom User Group session at KubeCon Barcelona 2019. The session, delivered by Dan Kohn (then Executive Director of CNCF) and Cheryl Hung (then Director of Ecosystem at CNCF), illustrated how OpenStack manages VM-based VNFs while Kubernetes orchestrates CNFs — with both layers operating on top of bare metal or cloud infrastructure. ONAP, a containerised Management and Network Orchestration (MANO) framework, was shown as an optional layer managed by Kubernetes.

The expectation, as discussed at that session, is that CNFs will become an increasingly dominant workload type. Kubernetes will be used to orchestrate both CNFs and, over time, VNFs — with VM isolation provided by projects such as KubeVirt, Virtlet, or OpenStack running on top of Kubernetes.

Three Approaches for Integrating Kubernetes and OpenStack

There is no single canonical way to combine Kubernetes and OpenStack. The right approach depends on your operational maturity, performance requirements, and how far along your cloud-native journey you are. Three main integration patterns have emerged:

Approach 1: Side-by-Side Co-existence

In this model, Kubernetes and OpenStack run independently on the same infrastructure. OpenStack manages VMs; Kubernetes manages containers. Neither system is aware of the other’s workloads.

Advantages: Relatively straightforward to implement; good raw performance for each workload type.

Disadvantages: No unified control plane. Resource planning, policy enforcement, and troubleshooting must be handled separately for each system. Diagnosing cross-workload performance problems is difficult, and applying consistent network or security policies across both environments is operationally expensive.

Approach 2: Kubernetes Clusters Running Inside OpenStack VMs

In this model, Kubernetes clusters are deployed within VMs that OpenStack manages. OpenStack acts as the infrastructure layer; Kubernetes operates inside it.

Advantages: Full multi-tenancy and security isolation from OpenStack can be applied to Kubernetes workloads. The existing OpenStack control plane and its networking and storage features remain available. This is a practical path for organisations with significant OpenStack investments.

Disadvantages: The additional virtualisation layer introduces performance overhead. Operational complexity increases because you must manage both the VMs hosting Kubernetes and the Kubernetes clusters themselves. Lifecycle management of the nested infrastructure requires additional automation tooling.

Approach 3: Kubernetes as the Unified Orchestration Layer

In this more forward-looking model, Kubernetes becomes the primary orchestration layer for both containers and VMs. OpenStack’s role may diminish or be restructured underneath Kubernetes. Projects like KubeVirt allow VMs to be scheduled and managed as Kubernetes workloads, alongside containers.

Advantages: A single control plane for all workloads. Full access to hardware accelerators and SmartNICs. Integrated VNF and CNF solutions become possible from a single orchestration layer.

Disadvantages: This approach demands advanced networking capabilities — service function chaining (SFC), provider networks, network segmentation — that OpenStack has developed over years and that Kubernetes-native networking is still maturing toward. Operational readiness and ecosystem support are prerequisites that many organisations have not yet reached.

Is This Kubernetes vs. OpenStack?

The framing of Kubernetes versus OpenStack is misleading. At events like VMworld 2019, Kubernetes featured heavily — across many sessions covering integration into nearly every aspect of IT infrastructure. This visibility has led some to conclude that Kubernetes will replace OpenStack.

That conclusion misreads the situation. OpenStack and Kubernetes solve overlapping but distinct problems. OpenStack has deep integrations with enterprise storage systems, physical networking hardware, and multi-tenant security models that Kubernetes does not replicate. Kubernetes provides declarative, self-healing container orchestration that OpenStack was not designed to deliver. Organisations that try to force either system to do everything tend to encounter real operational pain.

The practical path for most data centre operators is a deliberate combination of both — using each where it is strongest, with clear integration points between them.

Benefits of Running Kubernetes and OpenStack Together

When the integration is thoughtfully designed, the combined stack offers concrete operational advantages:

• Immediate modernisation path: Existing VM-based workloads continue to run unmodified under OpenStack while new containerised workloads are introduced through Kubernetes. This avoids a high-risk big-bang migration.
• Unified networking and security policy: Workloads on both systems can share a single network fabric and a consistent policy model, simplifying compliance and security operations.
• Enterprise storage and networking compatibility: OpenStack supports a wide range of enterprise storage backends and networking systems. Running Kubernetes on top of OpenStack allows containers to leverage the same storage and networking integrations without rebuilding them.
• Self-healing infrastructure: Kubernetes brings self-healing capabilities for containerised services. Integrated into an OpenStack environment, this can extend automated recovery to core services and compute nodes.
• Edge and far-edge deployments: The combination is well-suited to far-edge cloud scenarios where automated deployment, low latency, and compact footprints matter.
• Faster cluster provisioning: Work done by the team behind OpenStack-integrated Kubernetes installers has significantly reduced cluster bootstrap times. (The original article claims a reduction from 10–12 minutes to under 5 minutes for any cluster size using OpenStack Stein — see review notes.)

Relevance to 5G and Telecom Infrastructure

Telecom service providers moving toward 5G have broadly agreed that a cloud-native network core is a requirement, not an option. 5G architectures demand the kind of continuous deployment, automated scaling, and rapid failure recovery that containerised infrastructure enables.

OpenStack and Kubernetes are both mature, open-source frameworks with active communities and proven production deployments at scale. OpenStack brings the storage, networking, and multi-tenancy capabilities that telco environments need. Kubernetes brings the agility and automation that cloud-native service delivery demands. Together, they form a credible foundation for 5G core infrastructure — which is precisely why operators like AT&T have made long-term commitments to this combined approach.

Choosing Your Integration Strategy

The right integration approach depends on where your organisation sits on the cloud-native maturity curve:

• If you are early in your Kubernetes adoption and have a large existing OpenStack environment, Approach 2 (Kubernetes inside OpenStack VMs) is the lowest-risk starting point.
• If you are operating two separate teams and need quick wins without deep integration work, Approach 1 (co-existence) buys time while you plan a longer-term strategy.
• If you are building greenfield infrastructure or a new network edge deployment, Approach 3 (Kubernetes as the unified layer) is worth serious evaluation, particularly as KubeVirt matures.

None of these approaches is permanent. Most organisations will move through them progressively as their teams build expertise and as the upstream projects — particularly KubeVirt, Cluster API, and OpenStack’s own Kubernetes integrations — continue to mature.

Overview

Houston-based law firm Emerson Firm, PLLC announced on August 30, 2019 that it had filed a class action lawsuit in the United States District Court, Western District of Washington at Seattle, on behalf of more than 100 million Capital One customers in the United States whose personal and financial information was compromised in a major data breach. The breach was publicly disclosed by Capital One on July 29, 2019, and has been described as one of the largest data breaches ever to affect a financial services company.

The case is formally styled Amjed Ali Ababseh v. Capital One Financial Corporation, Capital One, N.A., Capital One Bank (USA), N.A., Amazon.com, Inc., Amazon Web Services, Inc., and GitHub, Inc., Civil Action No. 2:19-cv-1397.

What Happened: The Capital One Data Breach

Capital One is a major US bank holding company specializing in credit cards, auto loans, and consumer bank accounts. In the course of its business, Capital One collects extensive personal and financial information from customers and applicants, including Social Security numbers, credit scores, and account details.

According to Capital One’s disclosure, a hacker gained unauthorized access to customer data that Capital One had stored on Amazon Web Services (AWS) infrastructure. The stolen data was subsequently posted publicly on GitHub.com, making it accessible to anyone on the internet. The alleged perpetrator was identified as a former Amazon employee.

Who Is Named in the Lawsuit and Why

The lawsuit names three groups of defendants, each facing distinct allegations:

• Capital One (Capital One Financial Corporation, Capital One N.A., and Capital One Bank USA N.A.): As the entity that collected and was responsible for safeguarding customer data, Capital One faces allegations related to its failure to adequately protect sensitive information entrusted to it by customers and applicants.
• Amazon (Amazon.com, Inc. and Amazon Web Services, Inc.): The complaint alleges that the Amazon defendants failed to take adequate and reasonable measures to secure their cloud infrastructure and data systems against unauthorized access, contributing to the conditions that enabled the breach.
• GitHub, Inc.: GitHub is named in connection with the stolen data being posted and made publicly available on its platform.

Who May Be Affected

According to Emerson Firm, PLLC, any person who held a Capital One account or applied for a Capital One product since 2005 may have had their data compromised in this breach. The class is national in scope, and the firm states that affected individuals may seek relief regardless of which US state they reside in.

About the Plaintiff’s Legal Counsel

The plaintiff is represented by Emerson Firm, PLLC, a Houston, Texas-based law firm with additional offices in Little Rock, Arkansas. According to the firm, it and its predecessor firms have focused on complex commercial litigation for more than 38 years and have collectively recovered more than one billion dollars for consumers through class action litigation across the United States.

DevOps and Cloud Security Context

For DevOps and cloud engineering teams, this case is a significant reminder of the legal and business risks associated with misconfigured cloud environments. The Capital One breach was attributed to a misconfigured Web Application Firewall (WAF) on AWS, which allowed the attacker to exploit a Server-Side Request Forgery (SSRF) vulnerability and access IAM credentials — ultimately enabling bulk data access from S3 buckets.

Key security lessons highlighted by incidents like this one include:

• Principle of least privilege: IAM roles and service accounts should be scoped to the minimum permissions required. Over-permissioned roles dramatically increase the blast radius of a compromise.
• SSRF protections: Cloud-hosted applications should be hardened against SSRF attacks, which can be used to query internal metadata endpoints and steal credentials.
• Secrets and code hygiene on GitHub: Developers and organizations should enforce policies preventing sensitive data, credentials, or customer information from being committed or posted to public repositories.
• Cloud security posture management (CSPM): Continuous automated scanning of cloud configurations can detect misconfigurations before they are exploited.
• Incident response planning: Organizations storing regulated data must have clear, tested breach response procedures, including data notification obligations under applicable law.

The inclusion of AWS and GitHub as named defendants in this litigation underlines that cloud providers and platform operators are increasingly subject to legal scrutiny when their platforms are involved in data exposure events, even where the root misconfiguration originates with the customer.

Source

This article is based on a press release published by Emerson Firm, PLLC via GlobeNewswire on August 30, 2019.

What Is a Data Lake?

A data lake is a centralized repository designed to store structured, semi-structured, and unstructured data at scale, typically in raw format. Unlike a data warehouse, which enforces a schema before ingestion, a data lake follows a schema-on-read approach. This makes it flexible enough to support diverse workloads including machine learning, real-time analytics, and batch processing.

For machine learning in particular, co-locating training data with compute resources is critical for performance. Cloud providers address this by bundling storage and compute into cohesive data lake platforms.

AWS Lake Formation

AWS Lake Formation is Amazon’s managed service for building, securing, and managing data lakes. Under the hood, it relies on Amazon S3 as its primary storage layer. Lake Formation sits on top of S3 and adds a governance and cataloging layer — including fine-grained access controls, data cataloging via AWS Glue, and automated ETL (extract, transform, load) workflows.

Key capabilities of AWS Lake Formation include:

• Centralized access control: Column- and row-level security policies applied consistently across analytics services such as Amazon Athena, Redshift Spectrum, and EMR.
• Data ingestion and transformation: Built-in blueprints and integration with AWS Glue for crawling, cataloging, and transforming data from relational databases, application logs, and streaming sources.
• ML readiness: Direct compatibility with Amazon SageMaker and other AWS ML services, making it straightforward to feed cataloged datasets into training jobs.
• S3 storage tiers: Because Lake Formation uses S3, teams benefit from S3’s full range of storage classes — from S3 Standard to S3 Glacier — allowing cost optimization based on access frequency.

Azure Data Lake Storage

Microsoft’s Azure Data Lake Storage (ADLS) is a cloud-native storage service optimized for big data analytics workloads. ADLS Gen2, the current generation, combines the scalability of Azure Blob Storage with a hierarchical namespace and HDFS-compatible interface, making it compatible with the broad Hadoop ecosystem including tools like Apache Spark, Hive, and Pig.

Key capabilities of Azure Data Lake Storage include:

• Hadoop compatibility: The HDFS-compatible API means existing big data workloads can be migrated to Azure with minimal code changes.
• Azure HDInsight integration: HDInsight brings managed Hadoop, Spark, Kafka, and HBase clusters directly to your data lake, enabling large-scale distributed processing.
• Azure Data Factory: A fully managed ETL and data integration service that connects to hundreds of on-premises and cloud data sources, providing similar pipeline capabilities to AWS Glue.
• Role-based and ACL security: ADLS Gen2 supports both Azure RBAC and POSIX-style ACLs at the file and directory level.
• Synapse Analytics integration: Azure Synapse provides a unified analytics workspace combining data warehousing, big data, and data integration on top of ADLS.

Storage Pricing: A Direct Comparison

Storage cost is often the first metric teams evaluate, but pricing structures differ enough between the two providers that a direct comparison requires care. The figures below are drawn from the original 2019 source article and should be verified against current published pricing before any procurement decision, as cloud prices change frequently.

AWS S3 pricing (as referenced in the source, 2019):

• S3 Standard: ~$0.023 per GB for the first 50 TB/month, with lower rates for higher tiers.
• S3 Standard-Infrequent Access: approximately $0.0125 per GB.
• S3 Glacier: as low as ~$0.004 per GB per month for archival storage.

Azure Data Lake Storage Gen1 pricing (as referenced in the source, 2019):

• Committed pricing: approximately $2,900/month for 100 TB and $26,000/month for 1 PB.
• Pay-as-you-go: starting at approximately $0.039 per GB, decreasing in higher tiers.

Based on these figures, AWS S3 appears to have a lower per-GB storage cost than Azure Data Lake Gen1 at equivalent volumes. However, the total cost of ownership depends on far more than raw storage rates — compute costs, egress fees, support tiers, licensing, and the engineering time required to integrate and maintain each platform all factor in significantly.

ETL and Data Integration

Both platforms provide managed ETL services that allow teams to ingest, clean, and transform data before it reaches the lake or before it is consumed by analytics workloads.

AWS Glue is the primary ETL service in the AWS ecosystem. It automatically crawls data sources, infers schemas, populates the AWS Glue Data Catalog, and generates Spark-based ETL scripts. Lake Formation builds on this catalog to enforce access policies.

Azure Data Factory is Microsoft’s equivalent and supports a broader set of native connectors to on-premises systems, SaaS applications, and third-party databases. Its visual pipeline authoring environment appeals to teams with mixed engineering and analyst profiles.

Machine Learning and Analytics Ecosystem

The data lake is rarely the end destination — it feeds downstream analytics and ML workflows.

On AWS, Amazon SageMaker provides end-to-end ML capabilities including data labeling, model training, hyperparameter tuning, and managed deployment. SageMaker reads training data directly from S3, making Lake Formation a natural upstream dependency.

On Azure, Azure Machine Learning integrates with ADLS and Azure Synapse. Teams already invested in Microsoft tools — including Power BI for visualization and Azure Databricks for Spark-based processing — will find the Azure ecosystem cohesive.

If your organization has an existing AWS footprint or is already using SageMaker, Lake Formation is the path of least resistance. If your stack is Microsoft-heavy or you rely heavily on the Hadoop ecosystem, ADLS Gen2 with Synapse or HDInsight is likely a better fit.

Key Considerations Beyond Price

Storage cost per GB is an important data point, but it should not be the sole driver of your decision. Consider the following when evaluating either platform:

• Existing cloud investment: Migrating between cloud providers is expensive in terms of engineering time and egress fees. Extend your current platform where possible unless there is a compelling reason to switch.
• Ecosystem compatibility: Teams using Apache Spark, Hadoop, or Hive heavily may prefer ADLS Gen2’s HDFS-compatible interface. Teams already using AWS services will benefit from Lake Formation’s native integrations.
• Governance requirements: Both platforms offer fine-grained access control, but the implementation differs. Evaluate how each maps to your organization’s compliance and auditing requirements.
• Data ingress and egress costs: Uploading data to either cloud is typically free, but downloading or transferring between regions carries charges that can significantly affect total cost at petabyte scale.
• Support and SLAs: Enterprise support contracts, guaranteed uptime SLAs, and the availability of professional services should be evaluated alongside technical capabilities.

Bottom Line

AWS Lake Formation and Azure Data Lake Storage are both capable, production-grade platforms for building and managing data lakes at scale. Based on published storage pricing (as of the original 2019 comparison), AWS S3 offers a lower per-GB cost, which matters at petabyte scale. However, the better platform for your organization depends on your existing cloud investments, preferred analytics tooling, Hadoop ecosystem dependencies, and the specific regulatory and governance controls you need to enforce.

For teams starting from scratch, both platforms deserve a proof-of-concept evaluation. For teams already committed to one cloud provider, the switching cost almost always outweighs any storage price delta — focus instead on optimizing storage tiers and data lifecycle policies within your existing platform.

Original source article: datacenterknowledge.com (2019). Pricing figures cited are from the original 2019 article and may no longer reflect current pricing. Always verify costs against the official AWS and Azure pricing calculators before making procurement decisions.

The Data Privacy Challenge Is Global — and Growing

Data has become one of the most consequential resources of the modern economy. Yet unlike physical commodities, data crosses borders instantly and is subject to a patchwork of regulatory frameworks that differ dramatically by country and region. For enterprise software vendors that store and process customer data on a global scale, this creates a compliance landscape of extraordinary complexity.

ServiceNow operates in dozens of markets worldwide, and its customers span industries that are themselves tightly regulated. Keeping pace with the evolving legal requirements in each jurisdiction — while maintaining consistent, trustworthy data-handling practices — is a core operational challenge, not an afterthought.

According to Mark Cockerill, ServiceNow’s Head of Global Privacy, the company’s approach rests on three interdependent strategic pillars: Trust, Education, and Commerciality. Understanding each pillar — and how they interact — provides a useful model for any enterprise grappling with global data-privacy obligations.

A Fragmented Regulatory Landscape

Europe’s General Data Protection Regulation (GDPR) is the most widely recognised data-privacy framework, partly because its extraterritorial reach and steep penalties made it impossible for global businesses to ignore. Most enterprise technology and legal teams now have at least a working understanding of GDPR’s core requirements: lawful basis for processing, data-subject rights, breach notification timelines, and restrictions on international data transfers.

Beyond Europe, however, the picture becomes considerably more complicated. Countries including the United States, Canada, Australia, Singapore, Brazil, Japan, and South Korea each maintain distinct privacy regimes with their own definitions, obligations, and enforcement mechanisms. A data-handling practice that satisfies one framework may create liability under another.

These regulations are not static. Technology advances continuously expand what data can be collected and how it can be used, prompting legislators to respond. The California Consumer Privacy Act (CCPA) is a prominent example of sub-national regulation that introduced new consumer rights and business obligations in the United States, with a number of other US states subsequently pursuing their own privacy legislation.

For a global platform vendor like ServiceNow, staying ahead of this evolution is not optional — it is a prerequisite for continuing to serve enterprise customers.

The Processor vs. Controller Distinction

A critical legal distinction underpins ServiceNow’s approach to data privacy. Under frameworks such as GDPR, there is a meaningful difference between a data controller — the entity that determines the purposes and means of processing personal data — and a data processor — an entity that processes data on behalf of a controller.

ServiceNow operates primarily as a data processor for the services it provides to customers. Its customers, as controllers, retain responsibility for the data they bring into the platform. This does not, however, reduce ServiceNow’s compliance obligations or its responsibility for the security and integrity of data processed on its infrastructure.

Critically, this arrangement also means that data privacy is a shared responsibility. A breach or compliance failure anywhere in the processing chain can expose both the processor and the controller. Enterprise customers typically work with multiple IT vendors simultaneously, each of which may be classified as a data processor under applicable law. Managing those relationships — and ensuring no single vendor becomes the weak link — is a significant operational challenge for data owners.

Going Beyond the Bare Minimum

Minimum compliance — doing just enough to avoid regulatory penalties — is a tempting but ultimately self-defeating strategy. It signals to customers that privacy is a cost to be minimised rather than a value to be upheld, and it leaves organisations poorly positioned when regulations tighten or new frameworks emerge.

ServiceNow’s stated philosophy is to build privacy protections that are scalable and forward-looking, capable of accommodating regulatory advances that have not yet arrived. This requires:

• Designing products and workflows with privacy controls built in from the start, rather than retrofitted after the fact.
• Maintaining certifications and formal compliance attestations that give customers independent assurance of data-handling standards.
• Actively monitoring legislative developments across all operating jurisdictions and preparing customers in advance of changes taking effect.

Consumer awareness of data privacy is also rising rapidly. End users increasingly understand that they are handing over personal information and expect the organisations they deal with — and those organisations’ vendors — to handle it responsibly. Meeting that expectation is becoming as important to customer retention as product quality or price.

The Three Strategic Pillars

Pillar 1: Trust

Trust is the foundation on which the other pillars rest. It applies equally to internal relationships — between the privacy team and the rest of the organisation — and to external relationships with customers and regulators.

Building trust requires demonstrating genuine commitment rather than performing compliance for appearances. In practice, this means:

• Obtaining and maintaining relevant privacy and security certifications.
• Publishing clear, accurate policies that describe how data is handled.
• Responding transparently to customer inquiries and audits.
• Treating employee data with the same rigour applied to customer data.

Consistency is essential here. If different teams or individuals within the same organisation give different answers to the same privacy question, trust erodes. Internal alignment is therefore a prerequisite for external credibility.

Pillar 2: Education

Knowledge gaps are a leading cause of data-privacy failures. Employees who do not understand why a particular data-handling practice matters, or customers who are unaware of the controls available to them, introduce avoidable risk.

ServiceNow’s educational pillar addresses two audiences:

• Internal teams: All staff, regardless of department, receive training that creates a consistent understanding of privacy obligations and practices. This enables any customer-facing employee to give accurate, reliable guidance.
• Customers: Education extends beyond the vendor’s own walls. Helping customers understand what protections are in place, what additional measures they can implement (such as multi-factor authentication), and what their own responsibilities are as data controllers strengthens the overall security posture for both parties.

Privacy and security are, in this model, explicitly a joint responsibility — not something one party can outsource entirely to the other.

Pillar 3: Commerciality

Enterprise decisions must ultimately be sustainable from a business perspective, and privacy investments are no different. The commerciality pillar acknowledges that a risk-based approach — prioritising resources where the risk exposure is greatest — is both economically rational and operationally necessary.

Importantly, commerciality here does not mean cutting corners. Rather, it means that privacy investments are tied to tangible business outcomes: maintaining customer confidence, reducing the cost of regulatory incidents, enabling entry into new markets with strict privacy requirements, and differentiating the platform in competitive evaluations.

When trust and education are in place, commerciality becomes a mechanism for raising the bar further — using strong privacy practices as a competitive advantage rather than treating them as a tax on operations.

Practical Takeaways for Enterprise Teams

Whether you are evaluating ServiceNow as a platform or building your own organisation’s data-privacy programme, the three-pillar model offers a useful framework:

1. Map your regulatory exposure. Identify every jurisdiction in which your organisation collects or processes personal data and maintain a living inventory of applicable requirements.
2. Audit your vendor ecosystem. Treat each data processor in your supply chain as a potential compliance risk. Require evidence of certifications, conduct due diligence on sub-processors, and ensure data processing agreements are in place.
3. Invest in internal training. Compliance failures frequently originate from human error rather than technical gaps. Consistent, role-appropriate training is a cost-effective mitigation.
4. Design for future requirements. Build privacy controls that are extensible, so that adapting to new regulations does not require rebuilding systems from scratch.
5. Communicate proactively with customers. Transparency about your data-handling practices builds the trust that is increasingly a prerequisite for enterprise relationships.

Conclusion

Global data privacy is not a problem that can be solved once and forgotten. Regulations evolve, technology introduces new vectors for data acquisition and misuse, and customer expectations continue to rise. Enterprise software vendors — and the organisations that depend on them — must treat privacy compliance as a continuous operational discipline rather than a periodic checkbox exercise.

ServiceNow’s three-pillar approach — grounding all activity in trust, maintaining that trust through education, and making commercially rational decisions about where to invest — provides a replicable model for any organisation navigating the same complexity. The underlying principle is straightforward: privacy done well is not a burden. It is the foundation of durable customer relationships.

AWS Lake Formation makes it easy for customers to build secure data lakes in days instead of months

Panasonic, Amgen, and Alcon among customers using AWS Lake Formation SEATTLE–(BUSINESS WIRE)–Today, Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), announced the general availability of AWS Lake Formation, a fully managed service that makes it much easier for customers to build, secure, and manage data lakes. AWS Lake Formation simplifies and automates many of the complex manual steps usually required to create a data lake, including collecting, cleaning, and cataloging data, and securely making that data available for analytics. Customers can easily bring their data into a data lake from a variety of sources using pre-defined templates, automatically classify and prepare the data, and centrally define granular data access policies to govern access by the different groups within an organization. Customers can then analyze this data using their choice of AWS analytics and machine learning services, including Amazon Redshift, Amazon Athena, and AWS Glue, with Amazon EMR, Amazon QuickSight, and Amazon SageMaker following in the next few months. There are no additional charges required to use AWS Lake Formation, and customers pay only for the underlying AWS services used. To get started with AWS Lake Formation,

Customers want to be able to perform analytics and machine learning across all of their data, regardless of the format or where the data lives. A data lake removes data silos and allows data to reside in a central place so customers can more easily apply different types of analytics and machine learning across all of their data. Amazon Simple Storage Service (Amazon S3) has become a very popular place for customers to build data lakes because of its scale, cost-effectiveness, durability, and easy integration with AWS’s analytics and machine learning services. However, even with those significant benefits, building and managing a data lake can still be a complex and time-consuming process. Customers need to provision and configure storage, move data from disparate sources into the data lake, and extract the schema and add metadata tags to make it accessible from a searchable data catalog. In order to do so, customers must clean and prepare the data – including partitioning, indexing, and transforming the data – to optimize the performance and cost that comes with running analytics on the data. Then, they have to set up data access roles and enforce security policies across their storage and each of their different analytics engines, and update the security policies when permissions change or new end users are added. And, finally, customers are required to make the data available in a secure way to their data analysts so that they can analyze and process the data using any of the available analytics engines. These steps require customers to perform a lot of manual work, and as a result, most customers can take up to several months to set up a data lake.

AWS Lake Formation significantly simplifies the process and removes the heavy lifting from setting up a data lake. AWS Lake Formation automates manual, time-consuming steps, like provisioning and configuring storage, crawling the data to extract schema and metadata tags, automatically optimizing the partitioning of the data, and transforming the data into formats like Apache Parquet and ORC that are ideal for analytics. AWS Lake Formation cleans and deduplicates data using machine learning to improve data consistency and quality. To simplify data access and security, AWS Lake Formation provides a single, centralized place to set up and manage data access policies, governance, and auditing across Amazon S3 and multiple analytics engines. To reduce the time analysts and data scientists spend hunting down the right data set for their needs, AWS Lake Formation provides a central, searchable catalog which describes the available data sets and their appropriate business use. Customers can now easily access data from a single place and integrate with their choice of AWS analytics and machine learning services, including Amazon Redshift, Amazon Athena, and AWS Glue, with Amazon EMR, Amazon QuickSight, and Amazon SageMaker following in the next few months. With AWS Lake Formation customers can set up and begin using a data lake in days instead of months.

“Our customers tell us that Amazon S3 is the ideal place to house their data lakes, which is why AWS hosts more data lakes than anyone else – with tens of thousands and growing every day. They’ve also told us that they want it to be easier and faster to set up and manage their data lakes,” said Raju Gulabani, Vice President, Databases, Analytics, and Machine Learning, AWS. “That’s why we built AWS Lake Formation, so customers can spend more time learning from their data and innovating, rather than wrestling that data into functioning data lakes. AWS Lake Formation is available today and we’re excited to see how customers use it as one of the building blocks for growing and transforming their businesses and customer experiences.”

AWS Lake Formation is available today in US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland) with additional regions coming soon.

Panasonic Avionics Corporation is the world’s leading supplier of in-flight entertainment and communication systems. “We wanted to create a data platform with the ability to manage the security settings for all the different applications in our environment. With AWS Lake Formation, we can now define policies once and enforce them in the same way, everywhere, for multiple services we use, including AWS Glue and Amazon Athena,” said Anand Desikan, Director of Cloud and Data Services, Panasonic Avionics. “The enhanced level of control gives us secure access to data and meta-data for columns and tables, not just for bulk objects, which is an important part of our data security and governance standard.”

Amgen is the world’s largest independent biotechnology company. “At Amgen we’ve been heavy users of Amazon Redshift, Amazon EMR, and Databricks clusters for over three years. Setting up security and access controls for each AWS account, service, user, and data set at the level of detail that was required could be cumbersome,” said Kerby Johnson, Enterprise Data Lake Product Owner, Amgen. “AWS Lake Formation streamlines the process with a central point of control while also enabling us to manage who is using our data, and how, with more detail. AWS Lake Formation allows us to manage permissions on Amazon S3 objects like we would manage permissions on data in a database. Our users will be able to find, access, and analyze the data they need with the tools they prefer. This new workflow can make everyone more productive when using Amgen’s data.”

Alcon is a leader in innovation and development of life-changing vision and eye care products. “Like a lot of companies, we started our data lake initiative to get away from having inaccessible silos of data,” said Srinivas Ravilisetty, IT Analytics Lead, Alcon. ”With AWS Lake Formation we can quickly add access to existing Amazon S3 buckets and define what’s in them and how it can be used. The data remains in place in S3, but we have full control over it for other uses.”

Life360 is the world’s leading peace of mind service for families. The Life360 app brings families closer with smart features designed to protect and connect the people who matter most. “We wanted to use AWS Lake Formation to build our data lake for supporting location-based time-series data, and make it much easier to load data. The pre-fabricated blueprints helped get data into the data lake without our data engineering team having to write code from scratch, so they could focus on operationalizing ingest, not reinventing the wheel,” said Richard Chennault, Head of Cloud and Data Services, Life360, Inc. “With AWS Lake Formation we were able to quickly unlock data available in Amazon S3 and make it available to analyze across a broad spectrum of AWS data services. The data remains in place in Amazon S3, we can analyze it in many different ways, and we maintain full control over it.”

Zalando is Europe’s leading online platform for fashion and lifestyle. “As Europe’s most fashionable tech company, we work hard to find digital solutions for every aspect of the fashion journey,” said Alberto Miorin, Engineering Lead, Zalando SE. “AWS Lake Formation gave us a scalable central point of control for data access through Amazon Redshift that not only simplified the process, but improved it through granular control over how our data is being used. Now we can discover, access, and analyze data in our data lake with our preferred tools, and leverage it for business intelligence and data science. This streamlined workflow helps our executives make the right decisions on time, and fosters innovation through machine learning.”

Accenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology, and operations. “I focus on helping clients in their ‘Data on Cloud’ journey. Specific to that, we have seen that organizations are dealing with a lack of trusted data when they need to perform analytics on data coming from multiple sources,” said Namrata Maheshwary, Senior Architect for the Data Business Group, Accenture. “Data cleansing is a critical step in data analytics and can greatly impact the business outcome and decision making. The new features in AWS Lake Formation have been hugely beneficial to address the challenge of data veracity and securing access to the data lake. We found it tremendously useful to make use of the advanced machine learning techniques for data preparation to find matching records, clean, and deduplicate data from different data sources. This will help reduce the time, effort, and cost, while improving the quality and accuracy of the data in a customer’s data lakes.”

Quantiphi is an Artificial Intelligence and Big Data software and services company driven by the desire to solve complex business problems. Quantiphi specializes in building data lakes and AI solutions for customers to deliver quantifiable value. “AWS Lake Formation allows us to deliver a secure data lake with access to relevant data in days,” said Arnav Gupta, AWS Practice Lead, Quantiphi. “We now have the ability to deliver the best of both worlds for our customers – full security, plus simplified access to relevant data for their users to make decisions easily. Our customers can focus on making smarter, analysis-driven business decisions by tapping into a powerful, centralized data source.”

About Amazon Web Services

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Splunk Launches Industrial IoT Specialization to Connect Partners with Industrial Buyers

Splunk Inc. (NASDAQ: SPLK) announced in August 2019 that customers and partners across multiple industries were achieving measurable improvements in customer satisfaction, operational efficiency, and cost reduction through Splunk’s enterprise Internet of Things (IoT) solutions. Central to this announcement was the introduction of an Industrial IoT Specialization within the Splunk Partner+ Program — the first vertical specialization offered under that program.

The specialization is designed to help channel partners with deep industrial domain expertise build and deliver Splunk-based solutions tailored to industrial buyers — a segment with distinct data challenges compared to typical enterprise IT environments.

Why Industrial IoT Requires a Different Approach

Industrial environments generate data from sources that are fundamentally different from traditional IT infrastructure — SCADA systems, industrial control systems (ICS), sensors on physical assets, energy meters, and operational technology (OT) networks. This data is often siloed, difficult to correlate, and locked in proprietary formats that general-purpose analytics tools struggle to process efficiently.

Jim Gillespie, CEO of GrayMatter (one of the launch partners), described the challenge clearly:

“Operational data is complex and often gets stuck in organizational silos, so it’s exciting to see so many technological breakthroughs available to industrial customers that break down the silos, and provide a complete view of their data to make better business decisions. By partnering with Splunk, we can offer data combination solutions to industrial organizations that provide a simple and secure way to combine data from sources previously hard to correlate, whether it is energy usage, customer sentiment, weather or cyber security for OT information.”

— Jim Gillespie, CEO, GrayMatter

The Splunk Enterprise IoT Solution Stack

Splunk’s enterprise IoT offering at the time of this announcement combined three core components:

• Splunk® Enterprise — the core platform for ingesting, indexing, and searching machine data at scale.
• Splunk Machine Learning Toolkit (MLTK) — enabling predictive analytics and anomaly detection on top of operational data streams.
• Splunk Industrial Asset Intelligence (IAI) — a premium application providing visualizations and insights specifically tailored to industrial asset management and monitoring.

Together, these components are intended to help organizations shift from reactive operations — responding to failures after they occur — to predictive operations, where potential issues are identified and addressed before they cause downtime or safety incidents.

Real-World Use Cases: Customers Putting IoT Data to Work

Puget Sound Energy: Bridging OT and IT in a Utility Environment

Puget Sound Energy, a utility company operating in the Pacific Northwest, deployed Splunk to analyze data from its industrial control systems and SCADA integrations. The goal was to break down the traditional wall between Operational Technology (OT) and Information Technology (IT) teams — a common friction point in utilities organizations.

“Puget Sound Energy is excited to work with Splunk to help us bridge the gap between OT and IT, enabling genuine collaboration between both teams. With Splunk’s data analytics platform, we can better deliver services and manage infrastructure, ultimately enabling us to deliver a seamless experience for our customers.”

— Chris Perez, Advisor and IT Facilities Infrastructure Engineer, Puget Sound Energy

This OT/IT convergence use case is increasingly common across utilities, manufacturing, and critical infrastructure sectors, where historically separate teams manage physical assets and digital systems with little shared visibility.

Sapura Brazil: Real-Time Insights Across Remote Oil and Gas Assets

Sapura Brazil, described as one of the world’s largest integrated oil and gas services and solutions providers, uses Splunk’s enterprise IoT solutions to aggregate and analyze data from complex, geographically distributed systems. The primary benefit cited was the ability to receive real-time information from remote field sites at company headquarters, enabling faster decision-making.

“The use of Splunk in our assets enabled the company to receive real-time information from remote sites to the company’s headquarters, allowing us to take immediate actions when required, with unprecedented celerity. As a result, Sapura is better prepared to fulfill the industry’s high standards and to achieve our targets, not only economically speaking, but especially with regards to quality, health, safety and environment.”

— Rogerio Salbego, CEO, Sapura Brazil

In the oil and gas sector, the cost of unplanned downtime and the safety implications of delayed incident response make real-time data access a high-value capability — not merely a convenience.

Initial Industrial IoT Specialization Partners

At launch, the following partners achieved the Industrial IoT specialization within the Splunk Partner+ Program, each bringing domain expertise in industrial process management, asset management, and operational data:

• Computacenter AG & Co oHG
• ESE Engineering und Software-Entwicklung GmbH
• GrayMatter
• Malisko Engineering, Inc.
• Robotron Datenbank-Software GmbH
• Strategic Maintenance Solutions, Inc.

These partners work directly with shared Splunk customers to design and deliver solutions that address industrial-specific data challenges — from energy monitoring to OT cybersecurity to asset performance management.

Key Takeaways for DevOps and Platform Engineering Teams

For DevOps and platform engineers working in or adjacent to industrial environments, this announcement reflects several broader trends worth tracking:

• OT/IT convergence is accelerating. Tools like Splunk are increasingly being positioned as the bridge between traditionally siloed operational and information technology stacks.
• Vertical specialization in partner ecosystems matters. Generic systems integrators often lack the domain knowledge needed to map industrial data sources to analytics platforms effectively. Vendor-certified vertical specializations help customers identify qualified partners faster.
• Predictive maintenance and real-time monitoring are the primary IoT value drivers. The customer examples cited here — utility SCADA analysis and remote oil and gas asset monitoring — both center on reducing reactive responses in favor of proactive, data-driven operations.
• Security for OT environments is an emerging requirement. The reference to OT cybersecurity in GrayMatter’s comments signals that securing operational networks is increasingly part of the IoT data conversation, not a separate workstream.

About Splunk Inc.

Splunk Inc. (NASDAQ: SPLK) provides a platform for searching, monitoring, and analyzing machine-generated data. Its solutions are used across IT operations, security, and IoT use cases by organizations worldwide. At the time of this announcement in 2019, Splunk positioned its enterprise IoT suite — combining Splunk Enterprise, the Machine Learning Toolkit, and the Industrial Asset Intelligence app — as its primary offering for industrial and IoT customers.

Splunk, Splunk>, Listen to Your Data, The Engine for Machine Data, Splunk Cloud, Splunk Light and SPL are trademarks and registered trademarks of Splunk Inc. in the United States and other countries. All other brand names, product names, or trademarks belong to their respective owners. © 2019 Splunk Inc. All rights reserved.

Why Automated Code Review Matters

As software becomes central to virtually every industry, the volume of code that development teams must produce, review, and maintain has grown enormously. More code means more opportunity for bugs, security vulnerabilities, and technical debt to accumulate — and traditional code review processes struggle to keep pace.

Code review sits upstream of software testing. Where testing examines how software behaves from the outside, code review inspects the structure and logic of the source itself, ideally before changes are merged or deployed. In practice, this involves developers, peers, and managers collaborating to catch obvious errors early — a process that scales poorly as codebases grow.

Static analysis tools such as Lint have long been available to help, but they tend to focus on a narrow band of stylistic and formatting issues rather than deeper logic errors or security flaws. That gap is what Swiss startup DeepCode set out to close.

What DeepCode Does

DeepCode applies machine learning to the code review problem. Its models are trained on billions of lines of code drawn from public open source repositories — primarily GitHub — learning patterns from real-world projects, including earlier versions of files and the subsequent fixes that developers applied.

This approach allows DeepCode to go beyond syntax checking. According to cofounder and CEO Boris Paskalev, the system attempts to infer the intent behind a piece of code, not just whether it compiles or conforms to style guidelines. That means it can flag more serious issues such as:

• Cross-site scripting (XSS) vulnerabilities
• SQL injection risks
• Logic errors and known bug patterns
• Security anti-patterns learned from open source fixes

The training pipeline works by examining how bugs appeared in historical versions of code and how they were subsequently resolved. This creates what DeepCode describes as a live knowledge base of known bugs and their solutions, which the model applies when reviewing new code submissions.

Beyond pattern matching on known issues, DeepCode also uses predictive and inferential algorithms to surface problems in code that may not have been explicitly fixed in any training sample — extrapolating from what it has learned to flag novel risks.

How It Integrates into a Development Workflow

DeepCode integrates directly with version control platforms. Developers connect it to their GitHub or Bitbucket accounts for cloud-hosted projects, and the tool reviews each commit automatically, flagging potential issues as part of the normal pull request flow. GitLab is supported for on-premises deployments.

For teams that want deeper integration, DeepCode also exposes an API, allowing larger enterprises to embed its review capabilities into custom internal tooling. At the time of this reporting (2019), the company was also working on its first IDE plugin, which would bring inline suggestions directly into the developer’s editor.

The workflow, in summary:

1. Developer commits code to a connected repository.
2. DeepCode reviews each commit automatically.
3. Flagged issues are surfaced in the pull request interface or via API.
4. Developers receive suggested fixes, not just error locations.

Claimed Accuracy and Time Savings

The obvious question for any automated review tool is: how accurate is it? Paskalev stated that developers typically spend around 30% of their time finding and fixing bugs, and that DeepCode can save roughly half of that — representing a meaningful reduction in overhead for active development teams.

The argument for the machine learning approach is scale: because DeepCode learns from the global open source community rather than any single team’s experience, it can in principle identify a broader range of issues than any individual reviewer or small group of reviewers. That said, no automated tool achieves perfect accuracy, and human review remains part of the process for edge cases and novel code patterns.

Background: ETH Zurich Spin-Out with Deep Research Roots

DeepCode was founded in 2016 as a spin-out from ETH Zurich (Swiss Federal Institute of Technology). The founding team includes CTO Veselin Raychev, formerly a software engineer at Google and a PhD researcher at ETH, and adviser Martin Vechev, a professor at ETH whose research group focuses on reliable and secure software systems.

That academic lineage gives the company a credible foundation in program analysis and formal methods — fields that underpin serious static and dynamic analysis tooling.

Funding, Pricing, and Language Support

In August 2019, DeepCode announced a $4 million seed round led by Earlybird, with participation from 3VC and Btov Partners. The company had previously raised $1 million in an earlier seed round.

Alongside the funding announcement, DeepCode revised its pricing model:

• Free for open source projects and educational use
• Free for enterprise teams of up to 30 developers
• $20 per developer per month for cloud deployments beyond the free tier
• $50 per developer per month for on-premises deployments

At the time, DeepCode supported Java, JavaScript, and Python, with plans to expand to C#, PHP, and C/C++ using the new funding. Known open source users of the platform included Embark Framework, the European Environment Agency (EEA), and PyMedusa.

Context: The Broader AI Code Review Market

DeepCode was operating in a market that was attracting significant investment. The automated software testing market was estimated at $8.5 billion in 2018, with projections for continued growth. Alongside testing tools such as Functionize, Testim, and Mabl, code analysis and review was emerging as a distinct and investable category.

The broader context included Microsoft’s $7.5 billion acquisition of GitHub and significant venture funding for GitLab — signals that the infrastructure supporting software development workflows had become strategically valuable at the enterprise level.

Note: DeepCode was subsequently acquired by Snyk in 2020. Readers researching this technology today should look into Snyk Code, which incorporates the DeepCode engine, for current capabilities and pricing.

Relational vs. Non-Relational Cloud Databases

Before evaluating specific platforms, it helps to understand the two broad database categories you will encounter:

• Relational (SQL) databases organise data into tables with rows and columns, enforcing explicit relationships between datasets. Common engines include MySQL, PostgreSQL, Microsoft SQL Server, Oracle Database, and MariaDB.
• Non-relational (NoSQL) databases store data without a fixed schema, using document, key-value, wide-column, or graph structures. They are well suited for unstructured or rapidly evolving data and large-scale analytics workloads.

Most major cloud providers now offer managed services for both categories, letting you choose the right engine for each workload without managing the underlying infrastructure yourself.

Amazon Web Services – Amazon RDS and Aurora

Best for: Teams already invested in the AWS ecosystem who need fine-grained control over database configuration.

Amazon Relational Database Service (Amazon RDS) is AWS’s fully managed DBaaS offering. It removes the need to provision hardware or install database software, while still giving database administrators significant control over instance types, storage, parameter groups, and networking.

Supported Engines

• Amazon Aurora (MySQL- and PostgreSQL-compatible)
• PostgreSQL
• MySQL
• MariaDB
• Oracle Database
• Microsoft SQL Server

Pricing

AWS offers three primary pricing models for RDS:

• On-Demand Instances – Pay by the hour with no long-term commitment.
• Reserved Instances – Commit to a 1- or 3-year term for a significant discount.
• AWS Free Tier – 750 hours per month of db.t2.micro (or equivalent) Single-AZ usage for 12 months, applicable to MySQL, PostgreSQL, MariaDB, Oracle BYOL, and SQL Server Express.

Costs vary by instance class, storage type, data transfer, and Multi-AZ deployment. Use the AWS RDS pricing page and the AWS Pricing Calculator to model your expected spend before committing.

Key Considerations

• Amazon Aurora can offer higher throughput than standard MySQL or PostgreSQL on RDS, though this varies by workload.
• RDS is best operated by teams with existing AWS skills or a dedicated DBA; the range of configuration options can be overwhelming for teams new to the platform.
• Automated backups, point-in-time recovery, and Multi-AZ failover are available across all supported engines.

Microsoft Azure SQL Database

Best for: Organisations with existing Microsoft investments, .NET development stacks, or hybrid on-premises/cloud environments.

Azure SQL Database is Microsoft’s fully managed relational database service built on the SQL Server engine. It is a platform-as-a-service (PaaS) offering, meaning Microsoft handles patching, backups, high availability, and hardware maintenance.

Key Features

• High compatibility with on-premises SQL Server, simplifying migrations.
• Built-in machine learning and intelligent query processing.
• Support for Connection Libraries across multiple languages and drivers.
• Integrated with Azure Active Directory for identity and access management.
• Geo-replication and automatic failover groups for high availability.

Pricing

• Free trial: New Azure accounts receive a $200 credit valid for 30 days and access to a selection of services free for 12 months.
• Pay-as-you-go: Billed based on vCores or DTUs (Database Transaction Units), storage consumed, and backup retention.
• Serverless tier: Automatically scales compute and pauses when idle, suitable for intermittent workloads.

Key Considerations

• Some users have reported variable experiences with Azure support responsiveness; evaluate support plan tiers before committing.
• Azure also offers Cosmos DB (NoSQL), Azure Database for PostgreSQL, and Azure Database for MySQL for teams that need alternatives to SQL Server.

Oracle Cloud Database

Best for: Enterprises running Oracle workloads on-premises that want a cloud migration path with minimal re-platforming.

Oracle Cloud Database is part of Oracle Cloud Infrastructure (OCI). Oracle positions its Autonomous Database service as a self-driving database that applies machine learning to automate tuning, security patching, backups, and scaling without human intervention.

Key Features

• Multi-layer encryption enabled by default.
• Self-patching and self-tuning capabilities marketed under the Autonomous Database branding.
• On-demand capacity scaling without downtime.
• Supports Oracle Database, MySQL HeatWave, and NoSQL Database Cloud Service.
• Full integration with other Oracle Cloud services and on-premises Oracle environments.

Pricing

• Free trial: 30-day trial with $300 in credits.
• Pay-as-you-go: Billed per OCPU hour and storage consumed.
• Monthly Flex / Annual Universal Credits: Commit to a monthly or annual spend across OCI services, typically at a discount versus pay-as-you-go.

Key Considerations

• Oracle Cloud is the natural home for existing Oracle Database workloads; migrating Oracle databases to competing clouds often incurs additional licensing complexity.
• The Autonomous Database service reduces DBA operational burden significantly, but teams should validate which administrative controls are abstracted away before adopting it.

Rackspace Cloud Databases

Best for: Teams that want the scale of a major cloud provider combined with hands-on managed services support.

Rackspace is a managed cloud services provider rather than a hyperscaler. Its Cloud Databases product is built on OpenStack and provides managed MySQL, Percona Server, and MariaDB instances. Crucially, Rackspace also acts as a managed services layer on top of AWS, Azure, and VMware environments, making it an option for teams that want expert support without building an internal cloud operations practice.

Key Features

• Managed MySQL, Percona Server, and MariaDB deployments.
• Instance sizes up to 64 GB RAM; storage scaling up to 1 TB.
• Read replica support with the ability to add or remove replicas on demand.
• Scheduled backups and point-in-time restores.
• Container-based isolation designed to deliver near bare-metal I/O performance.
• 24/7 access to database specialists.

Key Considerations

• Rackspace’s primary differentiator is support quality; user feedback is generally positive on this dimension.
• Public documentation has historically been less comprehensive than AWS or Azure equivalents; lean on Rackspace’s support team for configuration guidance.
• Rackspace’s own Cloud Databases product supports a smaller range of engines than AWS RDS or Azure; teams requiring PostgreSQL, SQL Server, or NoSQL engines should explore Rackspace’s managed AWS or Azure offerings instead.

Other Cloud Database Platforms Worth Evaluating

Beyond the four platforms above, several other services merit attention depending on your specific workload requirements.

IBM Db2 on Cloud

IBM Db2 on Cloud is a fully managed SQL database service delivered through IBM Cloud. It is a natural choice for organisations with existing Db2 on-premises workloads. The service supports flexible scaling, automated security patching, and private VPN connectivity. It integrates with IBM’s broader data and AI platform, including Watson Studio.

Zoho Creator

Zoho Creator is positioned differently from the other platforms here. Rather than providing a managed database engine, it offers a low-code application builder with a cloud database backend. Small teams and business users can build custom database-driven applications using a drag-and-drop interface without writing SQL. It integrates natively with the broader Zoho suite of business applications.

Couchbase Capella

Couchbase is a distributed NoSQL document database that supports N1QL (a SQL-like query language for JSON documents). Its cloud managed service allows teams to run Couchbase clusters without managing server infrastructure. It includes built-in analytics, cross-datacenter replication, and enterprise-grade security. Couchbase is well suited to applications that need flexible JSON data models at scale, such as user profile stores, catalogue systems, and session management.

MongoDB Atlas

MongoDB Atlas is MongoDB’s fully managed cloud database service, available on AWS, Azure, and Google Cloud. It handles provisioning, scaling, backups, and monitoring automatically, allowing development teams to focus on application logic rather than database operations. Atlas supports global clusters across multiple cloud regions, automated failover, and on-demand scaling. It also provides Atlas Search (full-text search), Atlas Data Lake, and Charts for analytics — making it a broader data platform rather than just a database host.

Google Cloud Database Services

Google Cloud offers a portfolio of managed database services to cover both relational and non-relational workloads:

• Cloud SQL – Fully managed MySQL, PostgreSQL, and SQL Server instances.
• Cloud Spanner – A globally distributed, strongly consistent relational database designed for planet-scale OLTP workloads.
• Bigtable – A petabyte-scale, fully managed NoSQL wide-column store suited to large analytical and time-series workloads.
• Firestore – A serverless, scalable document database designed for mobile and web application backends.
• AlloyDB for PostgreSQL – A fully managed PostgreSQL-compatible service designed for demanding enterprise workloads.

Google Cloud’s database portfolio is particularly strong for data analytics use cases, especially when combined with BigQuery for warehousing and Dataflow for stream processing.

How to Choose a Cloud Database Platform

With so many managed options available, use the following criteria to narrow your selection:

• Engine compatibility: Which database engine does your application currently use, and does the platform support it natively?
• Workload type: OLTP, OLAP, document store, key-value, time-series — each has purpose-built services.
• Existing cloud footprint: Staying within a single cloud provider reduces networking costs and simplifies IAM and monitoring integration.
• Operational maturity: Fully managed services (Aurora Serverless, Azure SQL Serverless, Firestore) reduce DBA overhead; configurable managed services (RDS, Cloud SQL) give more control but require more expertise.
• Compliance and data residency: Verify that the service offers deployments in the regions you require and holds relevant certifications (SOC 2, ISO 27001, HIPAA eligibility, GDPR, etc.).
• Total cost of ownership: Use each provider’s pricing calculator and model realistic traffic patterns, including storage growth, data egress, and backup retention costs.

All of the major providers offer free tiers or trial credits — take advantage of these to run representative workloads before making a long-term commitment.

Introduction

Since its release in 2014, Kubernetes has seen remarkable adoption across data centers and cloud environments. As a container orchestration platform, it has proven capable of handling diverse IT workloads — from virtualized network functions to AI/ML pipelines and GPU-accelerated compute. Kubernetes has effectively become the de facto standard for enterprises scaling toward cloud-native architectures.

The next frontier for Kubernetes adoption is edge infrastructure: distributed environments with constrained hardware resources, intermittent connectivity to a central cloud, and thousands of nodes to manage simultaneously. Edge deployments serve as the processing layer closest to IoT sensors and end devices, making low latency, resilience, and centralized policy enforcement critical requirements.

This article explains why Kubernetes is well-suited to edge environments and walks through three architectural patterns the Kubernetes IoT Edge Working Group has proposed for deploying Kubernetes at the edge.

Why Kubernetes for Edge?

Edge nodes introduce a new infrastructure tier between IoT devices and the central cloud or on-premises data center. Operators need to manage workloads at the edge with the same level of automation and dynamism they apply to their cloud environments. Manually managing thousands of geographically distributed edge nodes is not operationally feasible at scale.

Kubernetes is a strong candidate for edge orchestration for several reasons:

• Infrastructure agnosticism: Kubernetes can schedule workloads across heterogeneous hardware — x86 servers, ARM devices, GPUs, and FPGAs — without requiring changes to the application layer.
• Unified operational model: A single control plane and API surface means the same policy definitions, RBAC rules, and deployment manifests can govern workloads across cloud, on-premises, and edge environments.
• Horizontal scalability: Kubernetes is designed to scale out, making it practical to manage a large fleet of edge nodes from a centralized control plane.
• High availability and self-healing: Kubernetes continuously monitors node and pod health, automatically rescheduling failed workloads. This is particularly important at the edge where physical access to nodes for manual intervention is difficult.
• Rapid application delivery: Container images can be pushed to edge nodes with the same CI/CD pipelines used for cloud services, enabling consistent and fast software updates across the fleet.

In a typical edge and IoT architecture, analytics and control plane services reside in the cloud. Data and instructions flow bidirectionally between cloud and edge. Kubernetes provides a common operational paradigm so that policies and rulesets can be applied uniformly across the entire infrastructure, or scoped narrowly to specific edge sites and nodes when needed.

How Can Kubernetes Be Used in Edge Architecture?

In March 2019, a Kubernetes IoT Edge Working Group was formed, comprising engineers from Red Hat, VMware, Futurewei, Google, Edgeworx, and other organizations. The group was established to discuss, design, and document approaches for running Kubernetes in edge and IoT use cases.

At their presentation during KubeCon Europe 2019, the working group outlined three primary architectural approaches for deploying Kubernetes at the edge. Each trade-off differs in where the control plane lives and how much resource overhead is imposed on the edge node itself.

A standard Kubernetes cluster consists of:

• Master (control plane): Exposes the API server, schedules workloads, and manages cluster state via etcd.
• Worker nodes: Run the container runtime (e.g., containerd or Docker), the kubelet agent that communicates with the master, and one or more pods — groups of tightly coupled containers.

The three approaches for edge scenarios build on this foundation in different ways.

Approach 1: Whole Cluster at the Edge

In this model, a complete, self-contained Kubernetes cluster runs directly on the edge node or a small cluster of edge nodes. Both the control plane and workloads are co-located at the edge site, reducing dependency on a persistent cloud connection.

The main challenge with this approach is resource overhead. Standard Kubernetes control plane components (API server, controller manager, scheduler, etcd) consume significant memory and CPU, which is scarce on edge hardware.

K3s (by Rancher) is the reference implementation for this approach. K3s packages Kubernetes into a single binary under 100 MB, removes legacy and non-essential components, replaces etcd with SQLite by default, and reduces minimum memory requirements substantially. This makes it viable to run a production-grade Kubernetes cluster on resource-constrained edge nodes such as ARM-based single-board computers or small industrial servers.

Approach 2: Cloud Control Plane with Edge Nodes (KubeEdge)

In this architecture, the Kubernetes control plane remains in the cloud (public or private), while lightweight edge agents manage containers and local resources on edge nodes. The edge nodes do not run a full control plane; instead, they run a thin agent that communicates with the cloud control plane and can operate autonomously during periods of network disconnection.

KubeEdge is the reference implementation for this model. Originally based on Huawei’s Intelligent Edge Fabric (IEF) IoT platform, KubeEdge extends Kubernetes to the edge by adding:

• An EdgeCore agent running on edge nodes that manages local pods and device communication.
• A CloudCore component running in the cloud that bridges the standard Kubernetes API with EdgeCore agents.
• Offline autonomy — edge nodes can continue running workloads even when the cloud connection is interrupted.

This approach reduces the resource footprint on edge nodes significantly while enabling centralized visibility and policy management. It is well-suited for large-scale deployments with many diverse edge sites.

Approach 3: Hierarchical Cloud Plus Edge Using Virtual Kubelet

In this model, a Virtual Kubelet running in the cloud presents edge nodes and their pods as an abstracted Kubernetes node to the control plane. The Virtual Kubelet intercepts scheduling decisions and delegates actual workload execution to edge infrastructure, without the edge nodes themselves being directly registered as standard Kubernetes nodes.

This approach offers the greatest flexibility in how edge resources are represented and managed. It allows edge infrastructure from different vendors or using different runtimes to be plugged into a standard Kubernetes control plane through a common adapter interface. Virtual Kubelet was originally designed to connect Kubernetes to serverless container platforms (such as Azure Container Instances), but the same abstraction layer is applicable to edge scenarios.

The trade-off is increased architectural complexity and a harder debugging experience when issues span the cloud-to-edge boundary.

Key Challenges Remaining

Despite its strengths, using Kubernetes for edge and IoT environments introduces real challenges that the community is actively working to address:

• Intermittent connectivity: Edge nodes may lose contact with the cloud control plane. Workloads must continue running and state must reconcile cleanly when connectivity is restored.
• Fleet management at scale: Managing thousands of edge nodes — each potentially running different hardware configurations — requires sophisticated tooling for configuration drift detection, automated updates, and rollback.
• Security at the edge: Edge nodes are physically accessible in ways that cloud servers are not, increasing the attack surface. mTLS between components, node attestation, and secrets management at the edge require careful design.
• Observability: Aggregating metrics, logs, and traces from thousands of distributed edge nodes into a coherent monitoring system adds significant data pipeline complexity.
• Resource constraints: Standard Kubernetes tooling and add-ons (Prometheus, Istio, etc.) may be too resource-heavy for constrained edge hardware, requiring lighter-weight alternatives.

Summary

Kubernetes brings a proven, infrastructure-agnostic orchestration model to edge environments, enabling teams to manage distributed edge workloads with the same tools and practices used in the cloud. Three primary architectural approaches exist for deploying Kubernetes at the edge:

1. Whole cluster at the edge — full Kubernetes on lightweight distributions like K3s, suitable for semi-autonomous sites.
2. Cloud control plane with edge agents — projects like KubeEdge keep the control plane centralized while running thin agents on edge nodes with offline autonomy.
3. Virtual Kubelet abstraction — edge resources are presented to a cloud control plane through a virtual node interface, maximizing flexibility.

The right approach depends on the available hardware resources at the edge, acceptable latency for control-plane communication, offline operation requirements, and the scale of the deployment. As the Kubernetes ecosystem continues to mature its edge capabilities — through projects like KubeEdge, K3s, and working groups within the CNCF — the operational model for managing edge infrastructure is converging toward the same Kubernetes-native patterns already established in the cloud.

What Is IT Infrastructure Monitoring?

IT infrastructure monitoring is the practice of continuously observing the health, performance, and availability of an organization’s underlying technology stack — servers, networks, storage, cloud workloads, and the services that run on them. Effective monitoring gives operations and DevOps teams early warning of degradation, enables capacity planning, and provides the data needed to meet SLA commitments.

As enterprises accelerate cloud adoption and distributed architectures, the monitoring market has grown accordingly. Organizations must now track on-premises hardware alongside containerized workloads, SaaS applications, and hybrid cloud environments, driving demand for unified monitoring platforms.

Market Overview

According to the Orbis Research report Global IT Infrastructure Monitoring Market (2019), the market was expected to sustain consistent growth through 2023. The research covers qualitative and quantitative dimensions including market size, competitive dynamics, regional performance, deployment types, and end-user verticals. Note that the specific revenue figures cited in the original report were placeholder values (listed as “XXX million $”) and are therefore not reproduced here.

Key Vendors and Platforms

The report identifies the following companies as notable participants in the global IT infrastructure monitoring space:

• CA Technologies – enterprise IT management and AIOps tooling
• Splunk – log aggregation, search, and operational intelligence
• Nagios Enterprises – open-source-rooted network and host monitoring
• AppDynamics – application performance monitoring with infrastructure correlation
• ScienceLogic – hybrid IT monitoring and service visibility
• Spiceworks – SMB-focused network monitoring and IT help desk
• Datadog – cloud-native observability covering metrics, traces, and logs
• SevOne – network performance management at scale
• PagerDuty – incident response and alerting platform
• Zabbix – open-source enterprise monitoring for networks and servers

This list reflects the competitive landscape as of mid-2019. The market has continued to evolve — several vendors have been acquired or merged since the report was published (for example, CA Technologies was acquired by Broadcom in 2018).

Deployment Models

The market is segmented by how monitoring software is deployed:

• On-premises – software installed and managed within the organization’s own data center. Preferred where data sovereignty, compliance, or air-gapped environments are a concern.
• Cloud / hosted (SaaS) – monitoring delivered as a managed service. Lower operational overhead and faster time-to-value, but requires sending telemetry data to a third-party platform.

By 2019, cloud-hosted monitoring was gaining significant traction as teams adopted DevOps practices and infrastructure-as-code, making agent-based SaaS tools easier to deploy at scale.

Industry Verticals

Demand for infrastructure monitoring tools is not uniform across industries. The report segments end-user adoption across the following verticals:

• IT and Telecommunications – highest adoption; monitoring is foundational to service delivery
• Healthcare – driven by uptime requirements for electronic health records and medical devices
• Transportation and Logistics – real-time visibility into fleet management and supply-chain systems
• Energy and Utilities – SCADA and operational technology (OT) monitoring needs
• Banking, Financial Services, and Insurance (BFSI) – regulatory compliance and zero-tolerance for transaction-system downtime

Regional Segmentation

The research covers performance across major global regions:

• North America (United States, Canada) – typically the largest revenue contributor due to early cloud adoption and mature DevOps practices
• Europe (Germany, UK, France, Italy) – strong growth driven by digital transformation initiatives and GDPR compliance requirements
• Asia-Pacific (China, Japan, India, South Korea) – fastest-growing region as enterprises modernize legacy infrastructure
• South America
• Middle East, Africa, and GCC

Report Structure and Pricing

The Orbis Research report was structured as modular sections purchasable individually or as a bundle. The sections covered definitions and methodology, detailed manufacturer profiles, regional analysis, product-type segmentation, industry verticals, channel analysis (direct sales vs. distributor), trend forecasting through 2023, cost structure, and downstream consumer analysis.

For current pricing and availability, contact Orbis Research directly, as report editions and pricing change over time.

Why Infrastructure Monitoring Still Matters

Regardless of market report specifics, the underlying demand drivers for infrastructure monitoring remain strong:

• Complexity growth – microservices, Kubernetes, and multi-cloud architectures generate vastly more telemetry than traditional monolithic apps.
• SRE adoption – site reliability engineering formalizes error budgets and SLOs, both of which require accurate, real-time monitoring data.
• Cost optimization – monitoring unused or over-provisioned cloud resources directly reduces infrastructure spend.
• Security posture – anomaly detection in infrastructure metrics is an early indicator of breaches or misconfigurations.

Teams evaluating monitoring platforms should assess coverage (metrics, logs, traces, events), integration depth with their existing stack, pricing model at scale, and the maturity of alerting and on-call workflow features.

Source: Orbis Research, “Global IT Infrastructure Monitoring Market,” syndicated via askindustrynews.com, June 24, 2019.

What Are Automotive Driving Simulators?

Automotive driving simulators are hardware and software platforms that replicate the sensory experience of operating a vehicle—including visual feedback, motion cues, and vehicle dynamics—without placing a driver or test vehicle on a real road. They are used across motorsport, passenger-car development, public transport training, and consumer entertainment. The platforms range from desktop software tools that model vehicle dynamics to full-motion hexapod systems capable of reproducing lateral, longitudinal, and vertical forces on a human driver.

Key Market Segments

By Product Type

• Modular Design Simulators – Configurable platforms that can be adapted to different vehicle types or research objectives by swapping cockpit modules, motion bases, or visual systems.
• Multi-Station Driving Simulators – Facilities that run several simulator seats simultaneously, commonly used in driver training centers and licensing authorities.
• Bus and Heavy-Vehicle Simulators – Purpose-built rigs that replicate the dimensions, controls, and dynamics of buses, trucks, or other commercial vehicles for professional driver training.
• Physical (High-Fidelity) Simulators – Large-scale motion systems, often used by OEMs and motorsport teams, that deliver the highest level of dynamic realism through multi-axis motion platforms.

By End-User / Application

• Entertainment – Theme parks, arcade centers, and consumer VR experiences.
• Training Institutions – Driving schools, police and emergency services, and military driver training programs.
• Transport Authorities – Municipal and national regulators using simulators for licensing assessment and road-safety research.
• Automotive R&D – OEMs and Tier-1 suppliers using simulators to evaluate vehicle dynamics, ADAS algorithms, and human–machine interface designs before physical prototypes exist.

Notable Vendors in the Space

Several specialist companies and larger industrial groups compete in this market. The following vendors are frequently cited in industry coverage:

• Ansible Motion (UK) – Known for its Kinematic Driver-in-the-Loop (DIL) simulators used in motorsport and OEM vehicle development. Their systems emphasise high-bandwidth motion fidelity.
• Cruden (Netherlands) – Supplies high-fidelity simulators to Formula 1 teams, OEMs, and motorsport organisations, with a strong focus on real-time vehicle dynamics software.
• Dallara (Italy) – The racing-car constructor has expanded into simulation technology, offering driver-in-the-loop platforms aligned with its motorsport heritage.
• Moog (USA) – A major motion-control and aerospace company whose simulation division produces motion bases and complete simulator solutions for automotive, defence, and training markets.
• Mechanical Simulation (USA) – Developer of CarSim, TruckSim, and BikeSim, widely used software tools for vehicle dynamics simulation that integrate with hardware-in-the-loop and driver-in-the-loop rigs.
• Oktal (Sogeclair Group) (France) – Provides SCANeR simulation software and integrated simulator systems for automotive safety, ADAS, and autonomous driving research.

Regional Landscape

Adoption of automotive driving simulators is global, but demand patterns differ by region:

• North America – Strong demand from OEM R&D, defence training programs, and a mature motorsport industry.
• Europe – Home to many leading simulator vendors and Formula 1 teams; stringent road-safety regulations also drive simulator use in driver training and licensing.
• Asia Pacific – Rapid growth fuelled by large automotive manufacturing bases in China, Japan, and South Korea, plus expanding driver-training infrastructure.
• Latin America, Middle East & Africa – Emerging adoption, primarily in transport-authority training and entertainment applications.

Factors Driving Market Growth

• ADAS and Autonomous Vehicle Development – Testing perception, decision-making, and safety systems in simulation is faster and safer than real-world testing, creating strong demand from OEMs and technology startups.
• Reduced Development Cost and Time – Identifying handling or HMI issues in simulation before a physical prototype is built lowers engineering rework costs.
• Driver Safety Training – Simulators allow learner drivers and professionals to practise high-risk scenarios (emergency braking, adverse weather) without real-world danger.
• Motorsport Performance Optimisation – Racing teams use simulators for driver familiarisation with new circuits and for setup iteration between races.
• Regulatory and Licensing Applications – Some jurisdictions are exploring or mandating simulator-based assessment components for commercial vehicle licences.

Market Restraints and Challenges

• High Capital Cost – Full-motion, high-fidelity simulators require substantial upfront investment in hardware, software, and facility space, limiting adoption among smaller organisations.
• Motion Sickness and Latency – Mismatches between visual and motion cues can cause simulator sickness, and minimising system latency remains an active engineering challenge.
• Correlation to Real-World Behaviour – Ensuring that simulator results accurately predict on-road or on-track performance requires continuous model validation work.
• Skilled Operator Requirement – Running and maintaining high-fidelity simulators demands specialised engineering expertise.

Competitive Dynamics

The competitive landscape spans hardware specialists, software-only vendors, and vertically integrated providers that supply both. Competitive differentiation tends to centre on motion-platform bandwidth and fidelity, the accuracy and breadth of the vehicle-dynamics model library, software integration with third-party tools (e.g., MATLAB/Simulink, CARLA, OpenDRIVE), and after-sales support and model-correlation services. Larger industrial groups (such as Moog) compete on breadth of product portfolio and global support infrastructure, while specialist firms (such as Ansible Motion and Cruden) compete on technical depth and motorsport credibility.

Summary

The automotive driving simulator market spans a wide range of use cases—from entertainment and driver licensing through to cutting-edge ADAS development and Formula 1 performance engineering. Growth is underpinned by the increasing complexity of modern vehicles, the cost advantages of virtual testing, and stricter safety requirements for both consumer and commercial vehicles. Vendors such as Ansible Motion, Cruden, Dallara, Moog, Mechanical Simulation, and Oktal each occupy distinct niches within this expanding ecosystem.

For primary market data and revenue forecasts, consult specialist industry research providers directly.

What Is a Software Container?

A software container is a lightweight, executable unit of software that packages an application together with everything it needs to run: libraries, configuration files, dependencies, and other binaries. The goal is to decouple an application from the underlying network infrastructure and operating system so that it behaves consistently regardless of where it runs — on a developer’s laptop, a staging server, or a production cloud environment.

Unlike traditional virtual machines (VMs), containers share the host operating system’s kernel rather than bundling a full OS of their own. This architecture makes containers significantly smaller and faster to start than VMs, and allows a single server to host many more container instances than equivalent VM workloads.

Containers vs. Virtualization: Key Differences

Understanding why containers have gained traction requires comparing them directly with hardware virtualization:

The trade-off is isolation: because containers share the host kernel, a kernel-level vulnerability can potentially affect all containers on that host. Hardware virtualization provides a stronger security boundary at the cost of greater resource consumption. This is an active area of innovation, with projects such as gVisor and Kata Containers working to close the security gap.

Core Use Cases Driving Adoption

Several application categories have emerged as primary drivers of enterprise container adoption:

• CI/CD pipelines: Containers provide consistent, reproducible build and test environments, reducing “works on my machine” problems and speeding up release cycles.
• Microservices architectures: Each service can be packaged, deployed, scaled, and updated independently, enabling teams to move faster without tight coupling.
• Monitoring and logging: Containerized agents can be deployed as sidecars or DaemonSets, giving operators uniform observability across heterogeneous workloads.
• Storage and data services: Stateful workloads increasingly run in containers using persistent volume abstractions provided by orchestration platforms.
• Networking and service mesh: Container-native networking tools manage traffic routing, load balancing, and inter-service communication at scale.
• Security tooling: Image scanning, runtime policy enforcement, and secrets management are a growing product category targeting containerized environments.
• Management and orchestration: Platforms such as Kubernetes automate container scheduling, self-healing, scaling, and rolling deployments.

Competitive Landscape and Key Players

The software container ecosystem spans runtime engines, orchestration platforms, registries, security tools, and managed cloud services. Notable participants at the time of the original market overview (2019) included:

• Docker Inc. — popularised application containers and the image format that became the basis of the OCI (Open Container Initiative) standard.
• CoreOS, Inc. — contributed rkt (an alternative container runtime) and the etcd distributed key-value store before being acquired by Red Hat in 2018.
• Codenvy, Inc. — offered cloud-based developer workspaces built on container technology (later acquired by Red Hat and folded into Eclipse Che).

The broader ecosystem also includes major cloud providers (AWS, Google Cloud, Microsoft Azure), platform vendors (Red Hat/IBM, VMware, SUSE), and a large number of open-source projects under the Cloud Native Computing Foundation (CNCF) umbrella.

Market Dynamics and Growth Factors

Several structural factors have sustained demand for container technology:

• Open-source momentum: The majority of foundational container tooling — Kubernetes, containerd, CRI-O, Prometheus, Envoy — is open source and vendor-neutral. Enterprise buyers have shown a clear preference for open standards over proprietary lock-in.
• Reduced licensing overhead: Sharing a single host OS kernel across many containers eliminates the per-VM operating system licensing cost that characterised earlier virtualisation strategies.
• Cloud-native application design: The shift toward microservices and twelve-factor applications naturally fits the container deployment model.
• Partnership ecosystem: System integrators, ISVs, and cloud providers have built managed container services (Amazon ECS/EKS, Google GKE, Azure AKS) that lower the operational burden for enterprise adopters.
• Startup and innovation activity: New entrants in security, observability, and developer experience continue to expand the ecosystem, often partnering with or being acquired by larger vendors.

Remaining Challenges

Despite strong growth, the container market still faces several unsolved problems that vendors are actively working on:

• Security: Container escape vulnerabilities, image supply-chain attacks, and misconfigured registries remain real risks. Runtime security tools (Falco, Aqua Security, Sysdig) have emerged to address detection and policy enforcement.
• Networking complexity: Multi-cluster and multi-cloud networking, service mesh overhead, and CNI plugin fragmentation add operational complexity.
• Stateful workloads: Running databases and other stateful services in containers requires careful storage configuration and is more complex than stateless deployments.
• Operational expertise: Kubernetes in particular has a steep learning curve. The shortage of skilled practitioners is a market constraint as well as an opportunity for training and managed-service providers.

Geographic Segmentation

Container adoption is global, but adoption patterns vary by region. North America, particularly the United States, has historically led in enterprise container deployments, driven by a mature cloud infrastructure and an active startup ecosystem. Europe follows, with growing investment in cloud-native platforms. Asia Pacific represents a fast-growing segment, with large technology markets in China, India, Japan, and South Korea increasing adoption. Latin America and Middle East & Africa are at earlier stages of adoption but show increasing interest from enterprises modernising legacy infrastructure.

Summary

Software containers have matured from a developer convenience into a foundational enterprise infrastructure technology. Their ability to package applications with all dependencies, run consistently across environments, and consume fewer resources than VMs underpins their widespread adoption. The market continues to evolve rapidly around orchestration (Kubernetes), security, observability, and managed cloud services. Organisations evaluating container adoption should weigh the operational complexity of orchestration platforms against the long-term benefits of consistent, portable, and resource-efficient application delivery.

Microsoft Opens Azure Regions in Abu Dhabi and Dubai

In June 2019, Microsoft became the first of the three leading U.S. cloud providers — ahead of AWS and Google Cloud — to operate data centers physically located in the Middle East. The launch introduced two new Azure cloud regions based in the United Arab Emirates (UAE): one in Abu Dhabi and one in Dubai.

With these regions now generally available, both Microsoft Azure and Microsoft Office 365 became accessible from in-country infrastructure for the first time. Microsoft also announced that Dynamics 365 and Power Platform were expected to reach general availability from the UAE regions by the end of 2019.

“The opening of the new cloud regions in Abu Dhabi and Dubai marks the first time Microsoft will deliver cloud services directly from data center locations in UAE and expands upon Microsoft’s existing investments in the Gulf and the wider Middle East region.”

— Tom Keane, Corporate Vice President, Azure Global, Microsoft

Why UAE-Based Regions Matter for the Region

Establishing local cloud regions addresses several critical requirements for government and enterprise customers in the UAE and broader Middle East:

• Data residency and compliance: Local data center presence allows government organizations and regulated enterprises to store data within UAE borders, helping them meet in-country data sovereignty regulations.
• Latency: Workloads running closer to end users and internal systems reduce network round-trip times — a practical benefit for real-time applications.
• Regulatory certification: Microsoft stated it became the first cloud provider in UAE to achieve the Dubai Electronic Security Center (DESC) certification, enabling Dubai government and semi-government entities to use the new regions for their workloads.

Economic and Workforce Impact

Microsoft cited an International Data Corporation (IDC) prediction that cloud services could contribute more than 500,000 jobs across the Middle East, including over 55,000 new jobs in the UAE through 2022. Tom Keane noted that the UAE cloud regions were expected to positively influence job creation, entrepreneurship, and economic growth across the region.

“An increasing number of studies suggest that the Fourth Industrial Revolution will be a net creator of jobs. We are working closely with governments in the region to ensure both businesses and individuals are appropriately skilled to take advantage of these opportunities.”

— Samer Abu-Ltaif, President, Microsoft Middle East and Africa

Cloud Adoption Trends in the Middle East

According to a Microsoft study released in January 2019, 70% of organizations in the Middle East planned to migrate their IT infrastructure to the cloud within the following year. Local Azure regions directly support that migration path by removing the data residency barrier that had previously limited adoption among government and regulated enterprise customers.

Cloud Society: Closing the Skills Gap

Alongside the infrastructure launch, Microsoft highlighted its Cloud Society initiative — a training program built on top of Microsoft Learn that had, at the time of this announcement, trained more than 150,000 IT professionals across the Middle East and Africa. The program is designed to address the cloud skills gap among IT workers in the region, which Microsoft views as a prerequisite for realizing the economic benefits of cloud adoption.

Competitive Context

The UAE region launch gave Microsoft a meaningful first-mover advantage over AWS and Google Cloud in the Gulf region as of mid-2019. For DevOps teams and architects evaluating cloud strategy for Middle East deployments at the time, Azure became the only hyperscaler option offering both in-country data residency and DESC certification — two non-negotiable requirements for Dubai public sector workloads.

Both AWS and Google Cloud have since announced their own Middle East expansion plans, but Microsoft’s early presence established Azure as the dominant platform for UAE public sector and enterprise customers during a critical adoption window.