Before defining elastic search. I give you a brief explanation that you are in the field of data science and big data and you may have heard about elastic search and you might be wondering that how does search engine technology helps you in extracting meaning from your data at scale. How do you get answers in milliseconds? Let’s talk about that it started off as a scalable lucence version.it adds a horizontally scalable search engine. There are shards and each “shard” is an inverted index of documents but not just for full-text search.it only can handle structure data, & can aggregate data quickly and often doing a faster solution than Hadoop/ spark /flink/etc.

Often, you listen to engineers. Their sorts of priorities and various forms include in it. Now, among all engineering streams. Elastic is an integral part of the elastic search engineer. Let’s talk about an elastic certified engineer. An elastic engineer possesses the ability and skills on performing a necessary task to build complete solutions of elastic search and monitoring the functions of install, configure, and manage the cluster, indexing data into the clusters, query, and analyzing the index data.

What are the benefits of elastic engineer?

 Getting ideas about the elastic engineer. Now, it’s time to take action on benefits. What’re benefits do you getting from elastic engineering and you just can’t say words for it. After the completion of the exam and certifications then you behave as you saying. There are some of the benefits according to the ideology of elastic engineer as categorized:

• Scalability
• Really fast
• Multilingual
• Document oriented
• Auto-completion & instant search
• Scheme free

Why we should learn elastic engineer?

If you are talking about the elastic engineer then first you should assemble the conceptual knowledge about why you are learning or necessity along with the cause? Through, the learning path of elastic certification. It’s proven to be fruitful in the sectors of IT entities and organizations. Due to its Performa-based courses/training along with the professionals. That will give you the using concept with skills, task building techniques on the elastic search on the basis of two courses which are packed with elastic search engineer I and elastic search engineer II. Its certification is based on the elastic search engineer exam and after completing the procedure of all. It will create awesome technological advances, feed awareness about the industry and its impacts on the environment. One of the prominent roles of elastic certification is the job opportunity, creativity, and innovation in favor of drastic enhances.

How our elastic engineer course/training would help?

After taking the course structure of elastic search engineer. It will create ideology about the elastic search and perform functionalities in the areas of sharding techniques & strategies, tuning for ingest speed, monitoring the stack, backing up your data, and query theory.

Getting command all over the ability and data experiences will make you job achiever, exam certification and build techniques to qualify in performance considerations, high availability, always use bulk, increase refresh, disable replicas, templates/mapping, monitoring, delete snapshots, general things to avoid, general things to do, and tools for console & search profiler for better technology development. These training courses will help you to understand the advances in elastic search, develop skills, better performances and produce high-paid salary job opportunities towards the IT infrastructure. In this matter, I would like to prefer this course in the industry of software development for your future goals.

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Earlier this week, Bengaluru-based analytics company Course5 Intelligence announced leadership changes across its business verticals.

The company has appointed Jayachandran KR as the senior vice president of AI Labs; Raman Khatri as vice president of sales for pharma analytics; Bhupinder Grewal as vice president, corporate strategy (FP and A); Imran Saeed as vice president, global head – CPG; Kapil Malhotra as VP, digital and advanced analytics; Sidharth Sivasailam as vice president of products; and Vishal Dhruve as the product head.

According to the State of Artificial Intelligence in India report, the hiring activities from the mid-junior to the mid-senior experience levels are on the rise, indicating companies’ interest towards developing capabilities in computer vision, robotics, and natural language processing etc.

Heer, we have listed the major leadership appointments across data science, machine learning and artificial intelligence roles of late.

CarDekho appoints tech leadership roles
CarDekho announced that it has appointed five senior executives in engineering and product leadership roles at its Gurugram office.

Manjeet Dahiya has been appointed as vice president – artificial intelligence, machine learning and data sciences. Sandeep Singh and Mayank Kapoor have been appointed as vice presidents of engineering. Dharmesh Gandhi as vice president of products, and Aditya Kumar as vice president of product strategy and analytics.

PayNearby appoints Vivek Viswanathan
Fintech startup PayNearby recently announced the appointment of Vivek Vishwanathan as chief technology officer. In his new role, he will work closely with the product, data, and analytics teams.

Vishwanathan has over two decades of experience in building enterprise-grade products. Prior to this, he was VP, product development at Blue Yonder. He has worked with companies like Setu, Ezetap and Stillwater (co-founder and CEO) heading the engineering and product development teams.

Majesco appoints Ravi Krishnan
New Jersey and Mumbai-based insurance software solutions company Majesco appointed Ravi Krishnan as chief technology officer (CTO) to oversee all Majesco SaaS platforms’ architectural and technical direction. At Majesco, Krishnan will handle end-to-end innovation across the portfolio of products with robust cloud-native architecture, alongside scaling engineering capabilities and leading digital transformation.

Previously, Krishnan was the principal solutions leader for worldwide healthcare and insurance at AWS. Before AWS, he was the chief digital officer for Innoveo; and CIO, SVP – Digital Product Innovation at Woodruff Sawyer. He had held executive roles at Kaiser Permanente, Verizon, Cognizant, GE Insurance and PwC.

Network18 appoints Siddharth Raj Jain
Digital news and entertainment firm Network18 recently announced the appointment of Siddharth Raj Jain as its CTO. Jain will be responsible for all technology functions for Network18 Digital.

Jain has over two decades of experience in technology and product development across fintech, ad tech, navigation and software development. Prior to this, he has worked with PayU, Infosys, Subex, Nokia (India) and Inmobi.

Zee appoints Nitin Mittal
Media and entertainment firm Zee Entertainment Enterprise appointed Nitin Mittal as the president of technology and data wing. Mittal will lead the technology and data teams and run the strategic initiatives in tech, data, artificial intelligence and machine learning to accelerate the company’s digital transformation.

In addition to this, Mittal will lead the engineering team and work closely with Amit Goenka, president of digital business and platforms, to enhance the customer experience and support the growth plans of the OTT platform (ZEE5) in India and across the world.

Cloud computing is quickly gaining ground in the business world and Microsoft Azure is taking the lead. About 84% of enterprises had a multi-cloud strategy in 2019. Gartner’s Top Strategic Trends for 2021 has established that organizations require a strong AI strategy to make sure that their projects don’t stall.

With cloud computing showing no signs of reducing in its application and Microsoft taking a strong lead, it is important to examine what Azure Cloud is and how it impacts businesses and what Microsoft would be doing with it in the future.

What is Microsoft Azure?
Azure Cloud is a cloud platform that provides both low-level cloud solutions and high-level infrastructure. The infrastructure makes it possible for companies to run their applications in a scalable, elastic, and redundant environment hosted in the cloud.

Signing up on the Azure Cloud platform gives you access to over 200 products and cloud services that are designed to offer new solutions to your business operations. It is designed to solve modern business challenges and help build a future that would be unimaginable without cloud computing.

With Microsoft Azure, you can build, run and manage applications across multiple clouds, on-premises, and at the edge with great tools of your liking.

Why Azure Cloud holds the future of cloud services?
Virtually everything in the digital world has a connection with the cloud in some way. As tech giants focus on developing new ways of organizing, processing, and presenting data, cloud computing is slowly becoming part and parcel of our everyday lives.

In the recent past, Microsoft Azure, Google Cloud, and Amazon Web Services have provided cloud computing and cloud storage services. Newer entrants such as Vultr and Linode have also made inroads into the space.

What do you think is the future of cloud computing? How will it affect future business strategies, and to what extent? Concepts such as decentralized cloud computing and serverless cloud may start trickling into your mind.

Infrastructure as a Service (IaaS)
Azure today is synonymous with Infrastructure as a Service. IaaS is the primary low-level public cloud service used to provide instant computing infrastructure. It is managed over the internet.

IaaS is unique because it is less expensive and is a simple way of managing physical servers and data center infrastructure. It offers essential storage, computing, and networking resources on demand.

With all the amazing computing benefits that come with it, adopting an IaaS solution for your organization helps reduce maintenance of on-premises data centers, gain real-time business insights and save money that would have been spent purchasing and maintaining hardware.

Organizations are keen on the scalability of their business operations, and more emphasis will be put on this in the future. IaaS solutions give businesses the flexibility to scale their IT resources up or down as their needs change. They also have a provision for you to quickly introduce new applications, further increasing the reliability of your infrastructure.

With IaaS, you will not incur the costs of buying and managing physical servers and data center infrastructure. Besides, you only have to pay for the resources you need since each one of them is offered separately.

Azure Data and Analytics
Azure data and analytics provide unified data governance that helps organizations do away with data challenges and shape their future.

Azure Cloud makes the future look bright by offering the general availability of Azure Synapse Analytics, a limitless analytics service that provides business insights with an emphasis on enterprise data warehousing, Big Data, and data integration.

Azure Synapse Analytics combines various capabilities, among them, machine learning, business intelligence, and data engineering. Simply put, Azure Synapse Analytics securely and seamlessly unifies your company’s data in one location.

You can get started with Azure’s machine learning features in this guide from Iteration Insights.

Data Security
No other player in the tech industry beats Azure Cloud in offering secure cloud computing. Microsoft leverages several years of being a productivity solution provider and being an enterprise operating system to provide top-shelf security to all the information hosted in their cloud products.

Microsoft Azure has put together several resources to ensure organizations’ on-site data centers are secure. They update their systems on a regular basis to make their systems immune to external threats.

Once you have purchased Azure Cloud services, you will gain access to all elements related to the security of your information. All their customers have access to the latest threat detection and prevention technologies.

There is a range of security solutions and architecture that come with Azure Cloud. They include Information Protection which enables organizations to classify and protect specific information, documents, and emails by applying labels to them, and Advanced Threat Protection and Threat Intelligence.

Azure Cloud threat monitoring agents generate real-time security alerts and threat intelligence reports which provide insights into the attackers, their motives, and their tactics.

Improved business sustainability and scalability
Many businesses will be expanding their operations in the future. If they want to do so without major hiccups, they should adopt Microsoft Azure Cloud technology which provides scalability to businesses.

Under normal circumstances, it is quite costly and time-consuming to expand a business and provide it with new infrastructure. Azure Cloud guarantees the availability of services throughout the year, thanks to its many data centers the world over. As if that is not enough, Microsoft is planning to add even more servers.

Azure Cloud boasts of having a physical infrastructure in every continent. This allows for replication in the case where an organization would like to expand beyond the borders of one continent.

Final thoughts
There is no doubt that Azure Cloud is a game-changer in cloud computing. The public cloud service, IaaS, for instance, is making cloud computing very easy while providing a simple way of managing physical servers and data center infrastructure. If you picture the future of cloud computing, you are probably thinking serverless cloud, enhanced data security, improved business analytics, and business scalability. Azure Cloud provides it all. It holds the future of cloud computing in its hands – or in the cloud – so to speak!

Why Use Kubernetes Secrets?

Containers frequently need sensitive data — passwords, OAuth tokens, TLS certificates, SSH keys — to integrate with databases, external APIs, and other services. Storing this data in plaintext inside a container image or a Pod spec creates several risks:

• Credentials are visible to anyone with access to the image registry or the YAML manifest.
• Secrets baked into images are hard to rotate without rebuilding and redeploying.
• Version control systems (Git, etc.) may inadvertently expose plaintext credentials.

Kubernetes Secrets decouple sensitive configuration from application code and container images, making it easier to rotate credentials and control access independently of deployments.

What Is a Kubernetes Secret?

A Kubernetes Secret is an API object that stores sensitive data as key-value pairs. According to the Kubernetes documentation, Secrets let you store and manage sensitive information such as passwords, OAuth tokens, and SSH keys — more safely and flexibly than embedding them verbatim in a Pod definition or container image.

A Secret can be made available to a Pod in two ways:

• Environment variable: The secret value is injected as an environment variable at container startup.
• Mounted volume: The secret is mounted as a file inside the container’s filesystem.

Secrets vs. ConfigMaps

Both ConfigMaps and Secrets store configuration data as key-value pairs, but they serve different purposes:

Use Secrets for credentials, tokens, and any data that must be kept confidential. Use ConfigMaps for non-sensitive configuration such as application themes, environment names, or base platform URLs.

How Kubernetes Secrets Work

Kubernetes provides two broad categories of secrets:

1. Built-in Secrets (Service Account Tokens)

Kubernetes automatically creates Service Account secrets and attaches them to Pods, giving containers the credentials they need to communicate with the Kubernetes API server. These are created and managed by the cluster and can be disabled or overridden if you manage API credentials manually.

2. Custom Secrets

You define and create custom secrets to store your own application-specific sensitive data. These can be created using kubectl, a YAML/JSON manifest, or a secrets management platform.

Important Security Consideration: etcd Storage

By default, Kubernetes stores Secrets in plaintext inside the cluster’s etcd datastore. Base64 encoding is not encryption — it is trivially reversible. This means:

• Anyone with direct access to etcd can read all secret values.
• If etcd communication is not secured with TLS, secrets can be exposed in transit during cluster synchronization.
• Any user or process with root access to a cluster node can potentially impersonate the kubelet and read secret data.

For production clusters, you should enable encryption at rest for etcd using Kubernetes’ built-in EncryptionConfiguration API, and enforce strict RBAC policies to limit who can read Secret objects.

Creating Custom Secrets

Method 1: Using kubectl

The quickest way to create a secret is with the kubectl create secret command. For example, to create a generic secret:

# Create a generic secret with literal values
kubectl create secret generic my-secret \
  --from-literal=username=admin \
  --from-literal=password='S3cur3P@ss'

# Create a secret from a file
kubectl create secret generic my-tls-secret \
  --from-file=tls.crt=./server.crt \
  --from-file=tls.key=./server.key

To create the secret in a specific namespace, add the --namespace flag:

kubectl create secret generic my-secret \
  --from-literal=password='S3cur3P@ss' \
  --namespace=production

To inspect the secret metadata without revealing the values:

kubectl describe secret my-secret
kubectl get secret my-secret -o yaml

Note: kubectl get and kubectl describe will not display the raw secret values — they show only the key names and the byte length of each value.

Method 2: Defining a Secret in a YAML Manifest

You can also define a Secret declaratively in a YAML file. The data field requires base64-encoded values, while the stringData field accepts plain strings and Kubernetes handles the encoding automatically.

apiVersion: v1
kind: Secret
metadata:
  name: my-secret
  namespace: production
type: Opaque
stringData:
  username: admin
  password: S3cur3P@ss

Apply the manifest with:

kubectl apply -f my-secret.yaml

Important: Do not commit Secret YAML files containing actual credentials to version control. Consider using a tool like SOPS or Sealed Secrets to encrypt manifests before committing them.

Using a Secret in a Pod

Here is an example of injecting a secret as environment variables in a Pod spec:

apiVersion: v1
kind: Pod
metadata:
  name: my-app
spec:
  containers:
    - name: my-container
      image: my-app:latest
      env:
        - name: DB_USERNAME
          valueFrom:
            secretKeyRef:
              name: my-secret
              key: username
        - name: DB_PASSWORD
          valueFrom:
            secretKeyRef:
              name: my-secret
              key: password

Cloud-Native Secret Management

If you are running Kubernetes on a managed cloud platform, consider integrating with the provider’s dedicated secrets management service. These services offer centralized secret storage, automatic rotation, fine-grained access control, and full audit logging — capabilities that go beyond what native Kubernetes Secrets provide out of the box.

• AWS Secrets Manager — Integrates with EKS via IAM roles for service accounts (IRSA) and the AWS Secrets and Configuration Provider (ASCP).
• Azure Key Vault — Integrates with AKS via the Secrets Store CSI Driver and Azure Workload Identity.
• Google Cloud Secret Manager — Integrates with GKE via Workload Identity and the Secret Manager add-on.
• HashiCorp Vault — A cloud-agnostic, self-hosted option widely used for dynamic secrets and fine-grained policy enforcement.

Using a dedicated secrets management platform gives your team centralized auditing, secret versioning, and rotation policies that are difficult to replicate with native Kubernetes Secrets alone.

Best Practices for Kubernetes Secrets Management

• Enable encryption at rest for etcd using Kubernetes EncryptionConfiguration.
• Use RBAC to restrict which service accounts and users can read specific Secrets.
• Avoid environment variables for highly sensitive data where possible — mounted volumes are slightly harder to accidentally expose via process inspection.
• Do not store Secret YAML manifests with real credentials in version control — use Sealed Secrets, SOPS, or a secrets manager instead.
• Rotate secrets regularly and update your Deployments to pick up the new values.
• Audit access to Secrets using Kubernetes audit logs or your cloud provider’s audit trail.
• Prefer cloud or external secrets managers for production workloads where audit, rotation, and access policy requirements are strict.

Summary

Kubernetes Secrets provide a foundational mechanism to decouple sensitive configuration from application code and container images. While they are easy to use, the default storage in etcd without encryption means you must layer on additional controls — RBAC, encryption at rest, and ideally an external secrets manager — to meet production security requirements. Understanding these trade-offs is the first step to building a robust secrets management strategy for your Kubernetes workloads.

The Rise of the xOps Mindset

DevOps methodology has become shorthand for a broader cultural shift in software delivery: breaking down silos between development and operations teams, automating repetitive work, and shipping higher-quality output faster. That cultural model has proven so effective that it has spawned a growing family of derivative practices—DevSecOps, DevTestOps, GitOps, and DataOps among them. Each variant inherits the core philosophy of DevOps and applies it to a specific domain.

Understanding what these practices share—and where they diverge—helps engineering and data teams decide which elements to adopt and in what order.

What All xOps Practices Have in Common

Regardless of the specific domain they address, xOps practices are linked by three foundational characteristics:

1. End-to-End Workflow Integration

In a traditional waterfall-style environment, production and distribution functions operate in isolation. xOps practices replace that separation with synchronized workflows that span the full lifecycle—from initial creation through to runtime in production. The goal is a continuous delivery loop where teams can ship incremental changes frequently, measure the results, and iterate. Internal communication and feedback loops are not afterthoughts; they are engineered into the process from the start.

2. Automation of Non-Creative Work

Automation is the engine that makes continuous delivery sustainable at scale. By automating repetitive, low-judgment tasks—test execution, change tracking, environment provisioning, deployment orchestration—teams reduce the risk of human error and eliminate many of the communication gaps that slow delivery. In a software context this typically means CI/CD pipelines. In a data context it means automated pipeline validation, data quality checks, and model promotion workflows.

3. Best Practices Baked Into Tooling

xOps tooling does more than execute tasks—it encodes organisational standards so that compliance with best practice is the path of least resistance, not an extra burden. Version control, peer review, automated testing, and observability are not optional add-ons; they are structural defaults. The specific implementation of best practice varies between organisations, but the shared goal is the continuous delivery of value while maintaining the safety to experiment and recover quickly from failures.

What Is DataOps?

DataOps applies the DevOps process model to data pipelines and data science workflows. In practical terms this means using automation, Agile methodology, and continuous integration and delivery to:

• Reduce the time spent diagnosing and fixing broken data pipelines.
• Accelerate the promotion of data science models from development into production.
• Improve data quality through systematic testing and version control.
• Make trusted data accessible to a wider audience within the organisation.

A useful mental model: just as DevOps treats software delivery as a continuously tested, continuously deployed pipeline, DataOps treats data as a product that flows through a versioned, tested, and monitored pipeline—ingested in raw form on one end and emerging as reliable, governed business intelligence on the other.

How DataOps Differs from DevOps

DevOps centres on the collaboration between software development and IT operations teams. DataOps broadens that collaboration significantly. Its stakeholders include data engineers, data analysts, data scientists, platform engineers, compliance officers, and—crucially—non-technical business users such as product managers and sales teams who consume the data outputs. This wider stakeholder map makes DataOps a more multidisciplinary undertaking than a typical DevOps implementation.

There are also technical differences. Data pipelines are inherently stateful and schema-dependent in ways that application code is not. Testing strategies must account for data drift, schema evolution, and referential integrity in addition to the logic-correctness tests familiar from software engineering. Versioning data assets presents challenges that do not exist when versioning source code.

Why DataOps Matters: The Scale of the Problem

Modern organisations draw on a large and rapidly growing number of data sources—structured databases, streaming event logs, third-party APIs, IoT sensors, SaaS platforms—each with its own format, latency characteristics, and governance requirements. Older data management systems and manual workflows were not designed for this volume or variety. The result is a common set of pain points:

• Data engineers spending the majority of their time on pipeline maintenance rather than new capability development.
• Analysts receiving data that is stale, inconsistent, or of uncertain provenance.
• Data science models that perform well in notebooks but fail or degrade in production.
• Compliance teams unable to trace where a given data element originated or how it was transformed.

DataOps addresses these pain points by applying the same systematic engineering discipline to data that DevOps applied to software.

Key Benefits of Adopting DataOps

Automation and Continuous Data Flow

When data pipelines are designed with the same automation and CI/CD standards applied to software delivery, data moves continuously and predictably from ingestion to consumption. Automated quality checks catch problems at the source rather than downstream, where they are far more expensive to fix. The practical result is a shorter time-to-insight: raw data reaches analysts and decision-makers as reliable, current information rather than as a batch report generated the previous night.

Cultural Shift: Democratising Data Access

DataOps changes who in an organisation is able to use data confidently. When data quality is systematically assured and lineage is transparent, non-technical stakeholders—sales teams evaluating market expansion, product managers prioritising roadmaps, finance teams modelling scenarios—can act on data without depending on an analyst to vouch for its reliability. This shift from data as a specialist resource to data as a shared organisational asset is one of the most significant cultural outcomes of a mature DataOps practice.

Improved Machine Learning Operations

Machine learning models are only as good as the data they are trained and evaluated on. When DataOps practices—version control for datasets, automated pipeline testing, continuous integration for feature engineering code—are applied upstream of model training, the quality and consistency of training data improves. Continuous delivery then extends to model deployment: new model versions can be promoted to production, monitored, and rolled back using the same patterns used to deploy application software. This intersection of DataOps and MLOps is where many organisations are finding significant leverage for their data science investment.

Getting Started: Practical Considerations

Adopting DataOps is not primarily a tooling exercise—it is a process and culture change, just as DevOps adoption is. Teams moving in this direction should consider the following:

• Start with observability. You cannot improve what you cannot measure. Instrument your existing data pipelines for failure rate, latency, and data quality metrics before attempting to automate or accelerate them.
• Version your data assets alongside your code. Treat schema definitions, transformation logic, and reference datasets as versioned artefacts, not ephemeral byproducts.
• Apply testing at every stage. Unit tests for transformation logic, integration tests for pipeline outputs, and data contract tests between producer and consumer systems all reduce the cost of change.
• Establish data ownership. Each dataset or pipeline should have a clearly identified owner who is accountable for its quality and availability—mirroring the service ownership model in DevOps.
• Engage non-technical stakeholders early. The cultural benefit of DataOps only materialises if the people who need data are involved in defining what quality and timeliness mean for their use cases.

Summary

DataOps is not simply DevOps with the word “software” replaced by “data.” It is a disciplined, cross-functional approach to data pipeline delivery that borrows the best-proven elements of DevOps—automation, CI/CD, continuous feedback, cultural transparency—and adapts them to the specific challenges of data engineering and data science. For organisations wrestling with data volume, quality, and velocity, DataOps offers a structured path from reactive pipeline maintenance to proactive, reliable, and democratised data delivery.

What Are Google Cloud Machine Images?

Google Cloud introduced a feature called Machine Images designed to make it significantly easier for system administrators and data scientists to set up, replicate, and maintain specialized virtual machine (VM) environments in the cloud.

At its core, a machine image stores all the information required to fully restore a Compute Engine VM instance. This goes beyond what Google Cloud’s older custom images feature offered. While custom images are limited to a single boot disk, machine images capture:

• All attached disks (boot and data disks)
• Instance configuration properties
• Instance metadata
• Associated IAM permissions

This comprehensive snapshot means a machine image represents a complete, reproducible environment — not just an OS image.

Why This Matters for Data Science and DevOps Teams

Data science workloads typically require carefully configured environments: specific library versions, GPU drivers, mounted datasets, and fine-tuned system settings. Manually reproducing these environments across multiple cloud instances is time-consuming and error-prone.

Machine images eliminate this friction. Once a VM has been configured to the desired state, teams can capture it as a golden machine image and use it as the authoritative baseline for spinning up additional instances — all configured identically to the source.

According to Ari Liberman, a product manager for Google Compute Engine, machine images are designed to sit at the heart of scalability, backup, and disaster recovery strategies. They use the same differential disk backup technology that powers incremental snapshots, making instance backups both reliable and cost-effective.

The “Golden Image” Workflow

Liberman illustrated the intended workflow with a practical example:

“Imagine that you’ve created and configured an instance as part of your web application, and you want to use it as the basis of other instances. With machine images, you can capture your web application exactly as you want it and save it as a golden machine image. You can then use that machine image to launch as many instances as you need, configured in the exact same way as your source instance. You can also share the machine image with other projects.”

Ari Liberman, Product Manager, Google Compute Engine

While the example references a web application, the same pattern applies directly to data science and ML engineering contexts — for example, capturing a configured Jupyter environment with CUDA drivers and Python dependencies installed, then sharing it across a team or replicating it for parallel experiment runs.

Override Functionality: Flexibility Without Sacrifice

Machine images also support override functionality, allowing individual properties of new VM instances to be selectively modified at launch time. This means teams can use a shared golden image as a base while still customizing things like machine type, network tags, or startup scripts on a per-instance basis — without altering the source image itself.

Key Use Cases

• Scalability: Rapidly launch multiple identical VM instances from a single source image to handle increased workloads.
• Backup and recovery: Capture point-in-time snapshots of full VM environments, including all attached disks, for reliable disaster recovery.
• Environment standardization: Share a validated, reproducible VM configuration across teams and projects.
• Onboarding: Provide new team members with a pre-configured environment instantly, reducing setup time.

Machine Images vs. Custom Images vs. Snapshots

Note: This comparison reflects the state of these features as of the March 2020 announcement. Google Cloud feature capabilities may have evolved since then.

How to Access Machine Images

At the time of announcement, Machine Images were available in beta and could be accessed through:

• The Google Cloud Console
• The gcloud CLI
• The Compute Engine REST API

For the current status of this feature and up-to-date documentation, refer to the official Google Cloud Machine Images documentation.

Company Achieves Milestone Just Eight Months After Joining the AWS Partner Network, Proving Technical Validity and Customer Success of dotData’s AutoML 2.0 Platform

dotData, focused on delivering full-cycle data science automation and operationalization for the enterprise, announced that it has achieved Amazon Web Services (AWS) Machine Learning (ML) Competency status.

dotData achieved AWS ML Competency status only eight months after joining the AWS Partner Network (APN). The certification recognizes dotData as an APN Partner that accelerates the full-cycle ML and data science process and provides validation that dotData has deep expertise in artificial intelligence (AI) and ML on AWS and can deliver their organization’s solutions seamlessly on AWS. dotData achieved AWS ML Competency status through a rigorous qualification process that included a strict validation of their capabilities to demonstrate technical proficiency and proven customer success, as well as a technical audit of its dotData Enterprise solution.

dotData provides solutions designed to improve the productivity of data science projects, which traditionally require extensive manual effort from valuable and skilled resources, by automating the full life-cycle of the data science process, from business raw data through feature engineering to implementation of ML in production utilizing its proprietary AI technologies.

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dotData’s AI-powered feature engineering automatically applies data transformation, cleansing, normalization, aggregation, and combination, and transforms hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects.

“By leveraging the ease of use of dotData Enterprise and the power of AWS, we were able to develop and deploy our first machine learning models in weeks, without the need for data scientists,” said Phillip Byrnes, Director of Business Intelligence, of US Electrical Services Inc. an independent electrical supply distributor headquartered in Middletown CT. “dotData Enterprise is like having a mathematician in a box, it helps us optimize our business by leveraging our existing Business Intelligence teams and resources. The product is that easy to use.”

“From its foundation, dotData’s vision has been to make AI and ML accessible to as many people in the enterprise as possible. We are proud of the successful experience of US Electrical Services’ BI team, which is a testament to our commitment to truly democratize AI in enterprise,” said Ryohei Fujimaki, founder and CEO of dotData. “Achieving AWS ML Competency status in just eight months recognizes our ability to deliver an outstanding product that dramatically accelerates the AI and ML initiatives of AWS users and maximizes their business impacts.”

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AWS ML Competency Partners provide solutions that help organizations solve their data challenges and enable ML and data science workflows. The program is designed to highlight APN Partners who have demonstrated technical proficiency in specialized solution areas and helps customers find the most qualified organizations with deep expertise and proven customer success.

dotData democratizes data science by enabling existing resources to perform data science tasks, making enterprise data science scalable and sustainable. dotData automates up to 100 percent of the data science workflow, enabling users to connect directly to their enterprise data sources to discover and evaluate millions of features from complex table structures and huge data sets with minimal user input. dotData is also designed to operationalize data science by producing both feature and ML scoring pipelines in production, which IT teams can then immediately integrate with business workflow. This can further automate the time-consuming and arduous process of maintaining the deployed pipeline to ensure repeatability as data changes over time. With the dotData GUI, the data science task becomes a five-minute operation, requiring neither significant data science experience nor SQL/Python/R coding.

What Was Announced

Acxiom, the data and technology company behind many enterprise marketing platforms, announced its selection as a Launch Partner for AWS Data Exchange — an Amazon Web Services (AWS) service designed to make it straightforward for AWS customers to find, subscribe to, and consume third-party data directly within the cloud.

Through the integration, AWS customers gain access to Acxiom’s aggregated marketing analytics datasets spanning regions across EMEA, APAC, and the Americas — all accessible from within the AWS environment rather than through disparate external systems.

What Is AWS Data Exchange?

AWS Data Exchange is an AWS marketplace service that allows organizations to discover, license, and ingest third-party datasets directly into their AWS workloads. It removes the friction typically associated with sourcing external data — eliminating manual downloads, custom integrations, and fragmented data pipelines — by giving subscribers a unified mechanism to access curated data products.

The service is particularly relevant for teams running analytics, machine-learning models, and data-driven applications on AWS, as it allows external datasets to be imported directly into services like Amazon S3, Amazon Redshift, or Amazon SageMaker without leaving the AWS ecosystem.

The Problem It Solves for Acxiom Customers

Before AWS Data Exchange, data consumers working with multiple providers — including Acxiom — had to retrieve data from numerous SFTP endpoints and manually consolidate those assets before any meaningful analysis could begin. This workflow created operational overhead that slowed time-to-insight and increased the risk of data handling errors.

Amanda Terry, Vice President of Publisher and Platform Partnerships at Acxiom, explained the impact at the time of the announcement:

“Before AWS Data Exchange, clients would have to go to multiple SFTP sites to download the information and bring those data assets together in one place. Having the ability to access these insights in one place gives our clients the ability to spend more time on the analysis needed to make good business decisions and create better customer experiences.”

Amanda Terry, VP of Publisher and Platform Partnerships, Acxiom

AWS Perspective: Simplifying Data Integration at Scale

Stephen Orban, General Manager of AWS Data Exchange at the time, framed the service around reducing integration complexity for organizations running diverse analytics workloads:

“We built AWS Data Exchange to offer organizations an easier way to find, subscribe to and integrate diverse data sets into the applications, analytics, and machine-learning models they’re running on AWS. We are delighted to be working with Acxiom to improve the efficiency of our shared customers’ analytics efforts.”

Stephen Orban, General Manager, AWS Data Exchange

Acxiom’s Data and Privacy Commitments

David Skinner, Managing Director of Channels and Alliances at Acxiom, emphasized that the datasets made available through AWS Data Exchange are ethically sourced and privacy-compliant. The announcement also pointed toward future expansions, including marketing analytics capabilities, data monetization services, and additional custom datasets to be added to the exchange.

“Acxiom is excited to make a selection of our ethically sourced and privacy-compliant data sets available to AWS customers. We also look forward to bringing more of Acxiom’s high-value products and services to AWS customers in the near future, including our marketing analytics capabilities, data monetization services and additional custom data sets.”

David Skinner, Managing Director of Channels and Alliances, Acxiom

Key Takeaways for DevOps and Data Engineering Teams

• Reduced pipeline complexity: Subscribing to data through AWS Data Exchange removes the need to build and maintain custom ingestion pipelines from external SFTP or API sources.
• Tighter cloud-native integration: Data lands directly in AWS storage services, making it immediately available to downstream ETL jobs, ML training pipelines, and BI tools running on AWS.
• Subscription-based access control: AWS Data Exchange provides a managed licensing layer, reducing the operational burden of managing data contracts and access credentials manually.
• Scalability: As data volumes grow or new regions are added, AWS-native tooling handles scaling without requiring re-architecture of the ingestion layer.

Video Overview

For more information on AWS Data Exchange, visit the AWS Data Exchange product page.

Why Cloud Computing Skills Matter

Cloud computing has become a core competency for modern IT organisations. Platforms from Amazon, Google, IBM, and Microsoft now underpin the infrastructure of enterprises across every sector, and demand for certified cloud professionals continues to outpace supply. Whether you are an experienced IT professional looking to pivot or a developer adding cloud-native skills, formal training and certification can significantly accelerate your career trajectory.

The following ten programmes represent a range of options — from postgraduate certificates to self-paced nanodegrees — available to learners based in India. This is not a ranking. Pricing and curriculum details reflect what was publicly available in late 2019 and should be treated as approximate; always confirm current information with the provider.

1. Jigsaw Academy (in association with Manipal Academy of Higher Education)

PG Certificate Program in Cloud Computing

• Duration: 6 months
• Cost: ₹88,500
• Delivery: Online (lab access included)
• Cities: Bangalore

Jigsaw Academy operates as part of the Manipal Education group and focuses on industry-relevant professional certification. This postgraduate certificate programme claims to be one of the only offerings in the market to include a dedicated lab experience for every task performed on cloud platforms.

The programme includes 300+ hours of online learning, with 70+ hours of live online lectures delivered on weekends by industry experts. Courseware is aligned with AWS Educate and covers Cloud Essentials, Cloud Foundry, Google Cloud, and OpenStack, with a specialisation track in AWS. Learners can use the programme to prepare for certifications including AWS Solutions Architect – Associate, AWS Developer – Associate, and AWS Certified SysOps Administrator.

2. Great Learning

Post Graduate Program in Cloud Computing (PGP CC)

• Duration: 6 months
• Cost: ₹1,25,000 + GST
• Delivery: Online

Great Learning is an ed-tech platform covering analytics, data science, machine learning, AI, cloud computing, DevOps, and full-stack development. Its PGP CC is designed to give candidates hands-on exposure to core cloud technologies including containers, microservices, cloud security, DevOps practices, cloud architecture, migration, and deployment — taught across AWS, Azure, and Google Cloud.

A notable differentiator is access to mentorship from industry professionals working at companies such as Dell, Microsoft, and Adobe. Group projects and peer interaction with cohorts that average 8+ years of professional experience are also part of the learning structure. Career guidance and interview preparation are included.

3. Simplilearn

Cloud Architect Master’s Program

• Duration: 352 hours (live virtual classes plus self-paced learning)
• Cost: ₹44,999
• Cities: Bangalore, Hyderabad, Pune, Mumbai, Gurgaon, Noida, and select international locations

Founded in 2010 and headquartered in San Francisco and Bangalore, Simplilearn offers outcome-based online training across digital technologies. Its Cloud Architect programme focuses on AWS and Microsoft Azure, covering the skills needed to design and deploy scalable, highly available, fault-tolerant, and reliable cloud applications.

Key topics include AWS CloudFormation, Azure Resource Manager, EC2, S3, Route 53, VPC, and Azure App Services. Completion prepares learners for cloud architect, cloud developer, and cloud engineer roles, as well as the associated vendor certification exams.

4. Coursera

Google Cloud Platform Fundamentals: Core Infrastructure

• Cost: Free enrolment (certificates may carry a fee)
• Delivery: Online (global)

Founded in 2012 by Stanford professors Daphne Koller and Andrew Ng, Coursera partners with 190+ universities and companies — including Yale, University of Pennsylvania, Google, and IBM — to deliver online courses, specialisations, and degrees.

This introductory GCP course covers key services including Google App Engine, Compute Engine, Kubernetes Engine, Cloud Storage, Cloud SQL, and BigQuery. It also introduces resource and policy management using Cloud Resource Manager and Cloud IAM. Hands-on labs provide practical exposure to GCP fundamentals.

Credits from this course can be applied toward several Coursera specialisations and professional certificate programmes, including:

• Developing Applications with Google Cloud Platform Specialisation
• Architecting with Google Kubernetes Engine Specialisation
• Cloud Engineering with GCP Professional Certificate
• Architecting with Google Compute Engine Specialisation
• Cloud Architecture with GCP Professional Certificate
• Networking in Google Cloud Platform Specialisation
• Security in Google Cloud Platform Specialisation

5. Udacity

Cloud Developer Nanodegree

• Cost: ₹22,299 per month (at time of publication)
• Delivery: Online (global)

Started as an experiment in online learning by Stanford instructors Sebastian Thrun and Peter Norvig, Udacity focuses on career advancement through mastery of in-demand technical skills. Its Cloud Developer Nanodegree begins with cloud fundamentals and AWS deployment, then progresses through building full-stack apps, leveraging microservices, Kubernetes clusters, and serverless architectures.

Core modules include:

• Cloud Foundations
• Full-Stack Apps on AWS
• Monolith to Microservices at Scale
• Development and Deployment of a Serverless Application

6. SpringPeople

Google Cloud Platform (GCP) for Developers Training

• Duration: 3 days (classroom) or 18 hours (live online)
• Cost: ₹19,995
• Cities: Bangalore, Delhi NCR, Gurgaon

Founded in 2009 by IIT alumni, SpringPeople is an enterprise IT training provider with 500+ courses. Its GCP for Developers course is practical and developer-focused, covering application development best practices, GCP client libraries, the Cloud SDK, Firebase SDK, and data storage options.

Specific topics include Cloud Datastore, Cloud Storage, Cloud Pub/Sub, Cloud Functions, Cloud Endpoints, Cloud Build, Container Registry, and performance monitoring with Google Cloud Operations (formerly Stackdriver). Training uses CloudLabs for hands-on practice, and real-time code analysis and feedback is a stated feature of the programme.

7. Edureka

AWS Architect Certification Training

• Duration: 15 days (weekday batch) or 5 weeks (weekend batch)
• Cost: ₹19,995
• Delivery: Online

Edureka is a global e-learning platform offering live instructor-led training with 24×7 lifetime support. Its AWS Architect Certification Training is designed around the AWS Certified Solutions Architect (CSA) exam and covers AWS architectural principles, scalability, reliability, and high availability.

Practical components include signing up for an AWS free-tier account, creating an S3 bucket via the Console and AWS CLI, and launching an EC2 instance. The course is geared toward IT professionals who want to qualify as an AWS Certified Solutions Architect.

8. edX

CS50 for Business Professionals (Harvard University)

• Duration: 6 weeks
• Cost: Free audit; ₹6,500 for a verified certificate
• Delivery: Online (global)

edX is a non-profit e-learning platform founded by MIT and Harvard, offering courses from 140+ institutions worldwide including Oxford, Columbia, and Berkeley. It provides free audit options and affordable certifications, and its MicroMasters programmes allow learners to stack credentials toward formal degrees.

The Harvard CS50 for Business Professionals course takes a top-down approach to computer science, emphasising high-level concepts and design decisions rather than low-level implementation. It is designed for managers, product managers, founders, and decision-makers. Topics covered include:

• Computational thinking
• Programming languages
• Internet technologies
• Web development
• Technology stacks
• Cloud computing

9. Inventateq

Introduction to Cloud Computing

• Duration: 2 months
• Cost: ₹15,000
• Cities: Bangalore and Chennai

Inventateq is a software training company focused on job-ready IT education. Its cloud computing course covers foundational concepts, cloud deployment and service models, and an introduction to AWS — including key vocabulary, services, and architectural concepts.

A notable commercial differentiator is its stated 100% job placement guarantee, backed by partnerships with 500+ IT companies. Trainees reportedly receive unlimited interview calls until placement is achieved, along with practical, domain-specific training.

10. CloudThat

All Access Learning Pass (AALP)

• Duration: 1 year
• Cost: ₹99,000
• Cities: Bengaluru, Mumbai, USA, UK

Established in 2012, CloudThat provides cloud training and consulting services for mid-market and enterprise clients. It holds expertise across AWS, Microsoft Azure, and Google Cloud Platform, and also covers big data and IoT.

The All Access Learning Pass is a subscription-style offering that allows IT professionals to attend any CloudThat course for one year. Learners can freely choose across technologies including AWS, Azure, Google Cloud, IoT, DevOps, blockchain, machine learning, and big data. The pass includes dedicated training coordinators, resume building support, and job placement assistance.

Choosing the Right Cloud Computing Course

When evaluating these or any cloud training programme, consider the following factors:

• Certification alignment: Does the programme map to a recognised vendor certification (AWS, Azure, GCP)?
• Hands-on labs: Theory alone is insufficient for cloud roles — look for programmes with practical lab components on real cloud environments.
• Delivery format: Self-paced, live online, and in-person classroom formats suit different learning styles and schedules.
• Cost vs. depth: Lower-cost introductory courses and free MOOCs are valuable for beginners, but postgraduate programmes offer more structured depth for career changers.
• Career support: Placement assistance, mentorship, and interview preparation vary significantly across providers.
• Currency of content: Cloud platforms evolve rapidly. Always verify that the curriculum is current before enrolling.

All pricing, course details, and availability information in this article reflects what was published in late 2019. Contact each provider directly for current offerings.

Google Cloud Takes Center Stage at RSNA 2019

The 105th Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA) opened on December 1, 2019, at McCormick Place in Chicago, Illinois. The week-long event brought together leading players in AI, machine learning, cloud computing, and automation serving the healthcare and life sciences sectors. Among the major exhibitors, Google Cloud stood out as a prominent presence, using the conference to demonstrate its expanding portfolio of cloud solutions tailored specifically for radiology and broader healthcare use cases.

What Google Cloud Was Showcasing

Google Cloud’s exhibition at RSNA 2019 centered on recent advances in cloud-native services designed for healthcare and life sciences organizations. Key areas of focus included:

• Managed DICOM Services: Tools enabling healthcare organizations to store, manage, and retrieve medical imaging data using the DICOM standard in a fully managed cloud environment.
• HIPAA-Supported Deployments: Cloud infrastructure and configurations aligned with HIPAA compliance requirements, aimed at reducing the compliance burden for healthcare IT teams.
• Data De-identification: Solutions designed to strip personally identifiable information from DICOM images, supporting privacy-safe data sharing and research workflows.
• Analytics and AI Integration: Emerging capabilities applying machine learning to radiology workflows, including image recognition and big data analytics.

Google also highlighted developer tooling relevant to healthcare cloud deployments. Cloud Code for popular IDEs was presented as a way to help development teams write, deploy, and debug cloud-native applications more efficiently. Stackdriver Logging (now part of Google Cloud’s operations suite) was highlighted for its advanced log searching and filtering capabilities within cloud-native environments.

Corporate Symposium: “Journey to the Cloud”

On December 3, Google Cloud hosted a corporate symposium titled Journey to the Cloud. The session featured insights and testimonials from Google Cloud customers and partners operating in the radiology and imaging space. The stated goals of the symposium were to drive innovation in the PACS (Picture Archiving and Communication System) industry, support real-world evidence generation, and accelerate the development of next-generation imaging solutions.

Cloud Services and Specializations on Display

Attendees could engage with Google Cloud representatives across several specialized solution areas relevant to radiology departments and healthcare IT teams:

• Disaster recovery planning and implementation in the cloud
• Vendor-neutral archiving (VNA) solutions
• Next-generation PACS solutions
• Teleradiology infrastructure and workflows
• Machine learning-based workflow optimization
• Cost-reduction strategies for Google Cloud deployments in healthcare settings

AI in Healthcare: Google and Kaggle’s Medical Imaging Competitions

One of the more notable initiatives at RSNA 2019 was Google’s collaboration with Kaggle, the data science and machine learning competition platform. Together, the two organizations hosted AI-based medical imaging competitions designed to spur innovation in clinical diagnostics.

A prominent competition focused on detecting intracranial hemorrhage — bleeding that occurs inside the cranium — from medical images. This is a clinically urgent problem: rapid, accurate detection of intracranial hemorrhage is critical for patient outcomes in emergency settings. Winning competitors worked with an enriched imaging dataset assembled by RSNA in collaboration with the American Society of Neuroradiology and MD.ai. The prize for the winning team was $25,000.

These competitions serve a dual purpose: they generate novel AI models that may have real clinical value, and they expand the community of machine learning practitioners working on healthcare problems.

About RSNA

The Radiological Society of North America (RSNA) is an international professional society representing radiologists, medical physicists, and other medical professionals. With more than 54,000 members across 146 countries, RSNA is one of the largest radiology organizations in the world. The society has been an active proponent of AI research in radiology, particularly in applying machine learning to the detection and classification of conditions such as intracranial hemorrhage — work that has the potential to prioritize urgent cases and accelerate clinical decision-making.

Why This Matters for DevOps and Cloud Engineering Teams in Healthcare

Google Cloud’s presence at RSNA 2019 reflects a broader trend: major cloud providers are investing heavily in healthcare-specific managed services, compliance tooling, and AI capabilities. For DevOps engineers working in regulated healthcare environments, the practical takeaways from RSNA 2019 include the maturation of managed DICOM APIs, tighter integration between cloud storage and ML pipelines for imaging data, and improved support for HIPAA-compliant infrastructure-as-code patterns. As these services stabilize, the operational complexity of building and maintaining radiology IT infrastructure in the cloud continues to decrease.

Overview

Michael Berthold, CEO and co-founder of the open-source data analytics platform KNIME, delivered the keynote at the KNIME Fall Summit 2019. His talk focused on the complete end-to-end data science cycle, which he divided into two top-level categories—Create and Productionize—each containing two distinct phases.

Phase 1 – Gather & Wrangle

The first phase covers data acquisition and preparation. KNIME addresses this through a rich library of connector and transformation nodes, as well as big data extensions that integrate third-party technologies directly into a visual workflow. Supported integrations mentioned by Berthold include:

• Amazon Redshift
• H2 (in-memory database)
• Apache Hive and Impala
• Apache Spark

At the time of the keynote, KNIME had also extended its cloud file system connectivity, making it easier to read from and write to cloud storage buckets alongside on-premises data sources.

Phase 2 – Model & Visualize

This phase covers model building, library integration, and result visualisation. KNIME provides domain-specific nodes for several verticals, including:

• Life science – specialised cheminformatics and bioinformatics nodes
• Text processing – NLP preprocessing and classification
• Image analysis – pixel-level processing workflows
• Time-series analysis – lag features, smoothing, and forecasting nodes

For practitioners who need more control, KNIME integrates popular ML frameworks such as H2O and XGBoost, and allows custom code to be embedded via R and Python script nodes. This hybrid approach—low-code visual pipelines plus escape hatches for custom code—means teams with mixed skill levels can collaborate on the same workflow.

Phase 3 – Deploy & Manage

Once a model is ready for production, KNIME provides mechanisms to expose it to end users and downstream systems:

• Analytics applications – interactive web-based frontends for non-technical consumers
• Web services – REST endpoints that expose workflows as APIs
• KNIME Model Process Factory – enables on-demand model retraining, automated initialisation, and monitoring and alerting

KNIME also addresses production governance concerns—versioning, backwards compatibility, compliance controls, and documented best practices—so data science teams can maintain audit trails as models evolve.

Phase 4 – Consume & Optimize

The final phase closes the loop by making ML workflows accessible to business consumers. KNIME supports two primary consumption modes:

• Workflow as a Service – direct deployment so downstream systems can invoke a model programmatically
• Analytics Application – a guided UI that lets non-data-scientists run and interact with a model without writing code

Three Generations of Data Science Automation

Berthold framed the maturity of ML automation in three generations:

First Generation – Pre-canned Pipelines

Clean data enters a fixed pipeline, a standard set of transformations is applied, and a model is produced. There is little to no customisation or feedback loop. This covers basic batch scoring scenarios.

Second Generation – Interactive and Continuous

Customised interactions are introduced in the Model & Visualize and Deploy & Manage phases. Continuous deployment practices are applied, and users can provide feedback that influences subsequent model versions.

Third Generation – Mix-and-Match Automation

The Gather & Wrangle and Consume & Optimize phases become fully automated. Model building still involves customised human interaction, while deployment introduces guided interaction—where the platform makes suggestions and the practitioner confirms them rather than configuring everything manually.

Data Science Abstraction Through Components

Berthold concluded by discussing how KNIME uses Components—encapsulated, reusable workflow segments—to raise the level of abstraction across the pipeline. Components can encapsulate complex logic for steps such as:

• Feature selection and engineering
• Model interpretation and explainability
• Model management (versioning, comparison)
• Model deployment to production

By packaging these steps as components, organisations can standardise how ML problems are solved internally, reducing duplication and lowering the barrier for less experienced team members.

Four Adoption Profiles for Data Science

Berthold suggested that organisations should align their data science investment with their actual business context. He described four adoption profiles:

1. Standard problems with limited business impact – Simple, commodity data science tools are sufficient; the cost of a custom solution outweighs the benefit.
2. Applications requiring no specialist in-house expertise – Pre-built ML models or no-code tools can solve the problem without a dedicated data science team.
3. Productionising data science for value creation – Continuous delivery of models, integration of user feedback, and adoption of new technologies become critical differentiators.
4. Deep innovation embedded in the business – The organisation explores cutting-edge ML/AI research, integrates proprietary R&D, and treats data science as a core strategic capability.

KNIME Product Ecosystem

For teams looking to implement the patterns Berthold described, KNIME offers several products that map to different points in the end-to-end cycle:

• KNIME Analytics Platform – The open-source desktop environment for building and testing data science workflows.
• KNIME Server – A commercial server product that adds scheduling, team collaboration, and centralised deployment. Includes the KNIME Web Portal for browser-based workflow execution.
• Data Science as a Service – A managed offering for organisations that do not want to host their own KNIME Server infrastructure.
• KNIME Hub – A community and enterprise repository for sharing and discovering workflow components.
• KNIME Cloud Cognitive Services – Integrations with cloud-based AI APIs (announced at the time of the summit).

For the most up-to-date product information and documentation, visit the official KNIME website.

What Is Azure Synapse Analytics?

Azure Synapse Analytics is a unified analytics service that brings together enterprise data warehousing and big data analytics under a single, integrated experience. The core motivation behind it, as articulated by Raghu Ramakrishnan, Microsoft’s CTO for Data, was to stop bolting features onto individual services and instead pull their core capabilities together:

“Instead of trying to add more features to each of our services, we decided to take a step back and figure out how to bring their core capabilities together to make it easy for customers to collect and analyze all of their increasingly diverse data, to break down data silos and work together more collaboratively.”

Raghu Ramakrishnan, Microsoft CTO for Data

Before Synapse, many cloud analytics vendors sold collection, retrieval, and analysis tools as separate products. These tools could not natively connect with one another and required users to learn and operate each one independently. Synapse aims to eliminate that fragmentation by allowing analysts, data engineers, and data scientists to manage and query both relational and unstructured data from within a single service — using standard SQL as a common interface.

According to Rohan Kumar, Corporate Vice President for Azure Data, the platform was built in direct response to customer feedback: mass cloud migration had delivered flexibility, elastic compute, and large-scale storage, but translating those advantages into tangible, data-driven business outcomes remained difficult because the tooling was too fragmented.

Key Features

Azure Synapse Analytics ships with a set of integrated capabilities spanning the full analytics lifecycle:

• Azure Synapse Studio: A unified web-based workspace for data prep, data management, data warehousing, big data analytics, and AI tasks — all in one place, without switching between tools.
• Power BI integration: Native connectivity to Power BI for building and publishing reports directly from the analytics workspace, reducing the steps required to share insights with business stakeholders.
• Azure Machine Learning integration: Built-in support for training and applying ML models against the data stored in Synapse, enabling intelligent applications without exporting data to a separate ML platform.
• Code-free data pipeline management: Visual tools for orchestrating data pipelines, making the platform accessible to users without deep coding expertise.
• Standard SQL support: The ability to query both relational data in the warehouse and unstructured data in data lakes using familiar SQL syntax, lowering the learning curve for existing SQL practitioners.
• Rewritten SQL query engine: Microsoft substantially rearchitected the SQL processing engine to be optimized for cloud-native workloads, handling large bursts of concurrent queries and diverse, petabyte-scale datasets.
• DevOps compatibility: The platform is designed to support DevOps workflows, including source control integration and CI/CD pipelines for data engineering assets.

What Developers and Data Engineers Gain

One of the most significant practical benefits for engineering teams is the elimination of large-scale data copying. Historically, deriving insights from both data warehouses and data lakes required copying terabytes of data between systems — a slow, expensive, and error-prone process. Azure Synapse Analytics is designed to query data in place across those sources, removing that bottleneck.

Microsoft also emphasized flexibility in the analytics engine layer, stating that enterprises will be able to choose their preferred analytics engine rather than being locked into a single provider’s approach.

The rewritten SQL engine is notable from an engineering standpoint. Microsoft described the effort as substantial — effectively rewriting core query processing logic to make it cloud-native, capable of scaling elastically to handle sudden large workloads and heterogeneous datasets simultaneously.

For data scientists, the unified workspace means an end-to-end data pipeline — from raw ingestion through transformation, modeling, and visualization — can be built and managed in a single environment, reducing context switching and pipeline complexity.

Accessibility for Non-Technical Users

Microsoft positioned Azure Synapse Analytics not only as a tool for data engineers and scientists but also as a platform accessible to business analysts with limited technical depth. The code-free pipeline tools and the Power BI integration mean that business professionals can locate, collect, and visualize data from multiple sources without requiring deep engineering knowledge. The goal, according to Microsoft, is to compress the time to deliver actionable business insights from weeks or months to a matter of days.

How Azure Synapse Compares to Alternatives

At the time of its announcement, Microsoft made performance claims comparing Azure Synapse Analytics to competing services. The company stated that Synapse could run equivalent queries over petabyte-scale datasets in significantly less time than Google BigQuery, and that the platform is built to support thousands of concurrent users — a scale that some competitors struggle with.

Azure Synapse does not operate in a vacuum. Competing and complementary solutions in the query-over-large-datasets space include:

• Google BigQuery: A fully managed, serverless data warehouse with strong SQL support and deep Google Cloud integration.
• AWS Athena: A serverless, interactive query service built on Presto that queries data directly in Amazon S3.
• Presto / Trino: Open-source distributed SQL query engines widely used for federated queries across heterogeneous data sources.
• Snowflake: A cloud-native data platform offering separation of storage and compute with strong multi-cloud support.

Each of these platforms has distinct architectural trade-offs. The key differentiator Microsoft is betting on with Synapse is tight integration across its existing Azure ecosystem — particularly Power BI and Azure Machine Learning — reducing the integration overhead that teams typically face when assembling multi-vendor analytics stacks.

Strategic Context for the Microsoft Azure Ecosystem

Azure Synapse Analytics reflects a broader trend among hyperscale cloud providers to consolidate their growing portfolios of analytics, big data, and AI services into unified platforms rather than continuing to expand a catalog of loosely related point solutions. By weaving Synapse into existing Azure products, Microsoft moves toward offering enterprise customers a more complete and coherent cloud data platform — one where the cost and complexity of integrating separate analytics, ML, and BI tools is substantially reduced.

At launch, the service was available in preview and not yet generally available. Teams evaluating it at that stage should verify current GA status, regional availability, and pricing tiers against the official Azure documentation, as these details will have evolved since the initial announcement.

APPS Stock Price Movement and Trading Volume

On October 31, 2019, Digital Turbine, Inc. (NASDAQ: APPS) was trading at approximately $7.01 per share, down $0.51 (roughly -6.77%) from its previous close of $7.52. The session recorded approximately 817,921 shares traded, below the stock’s average daily volume of around 2.18 million shares, suggesting a comparatively quieter session by volume despite the notable price decline.

With a float of approximately 73.19 million shares and a 10.75% price gain over the prior week, APPS was attracting attention from short-term traders. The one-year consensus analyst price target at the time stood at $7.17 per share, implying a modest upside of roughly 2.28% from the stock’s recent trading range of $7.45–$7.78. The range of individual analyst targets spanned from a low of $6.50 to a high of $8.00.

52-Week Range and Volatility Metrics

Digital Turbine’s 52-week high of $7.84 was reached on August 29, 2019, while its 52-week low of $1.28 was recorded on October 31, 2018—representing a decline of approximately -10.58% from that low-water mark to the session under review. The stock’s beta was reported at 1.42, indicating it was approximately 42% more volatile than the broader market. A beta above 1.0 is generally interpreted as a signal that the stock amplifies market moves in both directions, which is a key consideration for risk-aware traders.

The median analyst price target at this time was set at $7.50, slightly below the one-year high the stock had achieved in that period.

Technical Indicators: RSI and Stochastic Oscillator

Two widely followed momentum indicators offered mixed signals for APPS at the time:

• Relative Strength Index (RSI): The two-week RSI reading stood at 52.78. Values between 30 and 70 are conventionally considered neutral territory, suggesting the stock was neither overbought nor oversold at that moment.
• Stochastic Oscillator: The stochastic reading was reported at 82.71%. Readings above 80 are typically interpreted as indicating overbought conditions—the original article described this as “oversold,” which appears to be an error in the source material. A stochastic above 80 conventionally signals overbought, not oversold, conditions.

MACD and Chart-Based Support and Resistance Levels

At the time of writing, the Moving Average Convergence Divergence (MACD) indicator for APPS showed a reading of 0.35, placing the stock in positive (green) territory. When the MACD is above the zero line, the short-term moving average is above the long-term moving average, which is interpreted as a signal of upward price momentum. Traders typically watch for crossings of the zero line as potential buy or sell signals.

Based on technical chart analysis at the time:

• Upside target range: $7.72–$7.91 per share
• Key support level: $7.39 per share — a break below this level was viewed as a signal of weakening market structure
• Downside target (if support breaks): approximately $7.25 per share

Analyst Ratings and Coverage

Several brokerage firms had issued ratings on APPS in the months leading up to this snapshot:

• Canaccord Genuity initiated coverage with a Buy rating, published June 27 (year not specified in the source, assumed 2019).
• ROTH Capital upgraded APPS from Neutral to Buy in a flash note issued March 30 (assumed 2019).
• ROTH Capital subsequently downgraded the stock back to Neutral from Buy in a research note dated September 26 (assumed 2019).

Across the six analysts then tracking the stock, the average rating was approximately 2.1 on a standard scale, tilting toward the bullish end. All six analysts rated APPS as either a Buy or Strong Buy; none issued a Hold or Sell recommendation at that point in time.

Valuation Multiples

At the time of publication, APPS was trading at approximately 35.81x forward 12-month consensus EPS estimates. The stock’s Price-to-Book (P/B) ratio stood at 16.8, compared to an industry average of 7.9, indicating that the market was pricing APPS at a significant premium relative to its peers on a book-value basis. The trailing P/E ratio was listed as 0, likely reflecting that the company was not yet generating positive net earnings on a trailing basis.

Revenue and Earnings Forecasts

Based on consensus estimates available in October 2019, analysts were projecting the following for Digital Turbine:

• Current-quarter revenue: Projected to grow approximately 31.1% to reach $31.27 million
• Full-year revenue: Expected to increase roughly 28.1%, from approximately $103.57 million to $132.67 million
• Current-quarter adjusted EPS: Forecast to surge approximately 200% to $0.03 per share
• Full-year EPS: Projected to climb approximately 125% to $0.18 per share

These figures reflected Wall Street expectations at a specific point in time and may differ significantly from actual reported results.

This article is a historical reconstruction of market commentary from October 31, 2019. It does not constitute financial advice. Always consult up-to-date sources and a qualified financial adviser before making investment decisions.

This article covers the history of the serverless model, how AWS Lambda implements Functions as a Service (FaaS), and the hard constraints you need to design around before going all-in on Lambda.

The History of Serverless Computing

To understand serverless, it helps to trace the evolution of how applications have been hosted and operated.

On-Premises: Full Ownership, Full Burden

Early application deployment meant physically racking servers, configuring operating systems, managing hardware failure, and planning capacity months in advance. Teams required dedicated infrastructure staff, and delivery cycles were slow. Every scaling decision was a capital expenditure.

Infrastructure as a Service (IaaS)

The shift to rented virtual machines—Infrastructure as a Service—was a significant improvement. Providers took responsibility for the physical hardware, redundancy, and data center operations. Teams could provision a server in minutes rather than weeks, and costs shifted from capital to operational expenditure.

IaaS remains widely used today, particularly for complex workloads that require specific OS configurations, legacy software stacks, or fine-grained network control. But engineering teams still owned everything above the hypervisor: the OS, runtime, middleware, and application stack.

Platform as a Service (PaaS)

Platform as a Service abstracted the operating system and runtime away from developers. Services such as Google App Engine (launched 2008) and AWS Elastic Beanstalk (launched 2011) let engineers deploy a Python, Java, or PHP application without configuring the underlying environment. Scaling policies, patching, and load balancing became the platform’s responsibility.

PaaS shifted the engineering focus from infrastructure to application business logic—a meaningful productivity gain for teams building standard web applications.

Serverless and Functions as a Service (FaaS)

Serverless is the next step in this progression. Martin Fowler describes serverless architectures as application designs that incorporate third-party Backend as a Service (BaaS) providers and/or custom code run in managed, ephemeral containers on a Functions as a Service (FaaS) platform.

In practical terms: if a significant portion of your application’s business logic is delegated to managed third-party services—authentication, storage, messaging, payments—you are already operating in a serverless style. The FaaS component takes this further by letting you deploy individual functions that execute in response to events, with no server provisioning at all.

AWS Lambda, launched in 2015, was the event that brought FaaS into mainstream use for production workloads.

AWS Lambda: How It Works

AWS Lambda is a managed compute service that executes your code in response to events. You supply the function code and its dependencies; AWS handles everything else—availability, auto-scaling, OS patching, and hardware maintenance.

The Billing Model

Lambda uses a pay-per-use pricing model. You are charged based on two dimensions:

• Number of invocations — each time your function is triggered counts as one request.
• Duration — the time your function runs, measured in milliseconds and rounded up to the nearest 1 ms increment (note: billing granularity has changed over Lambda’s lifetime; verify current pricing in the AWS documentation).

When a function is not executing, you pay nothing. This makes Lambda particularly cost-effective for workloads with irregular or spiky traffic patterns, compared to running an always-on EC2 instance.

What a Lambda Function Is

A Lambda function is a discrete unit of code responsible for handling one type of event or task. You package the code with its dependencies and upload it to AWS. When an event occurs, Lambda instantiates a container, runs the handler, and then exits—either returning a response or completing silently.

A typical function might:

• Receive an HTTP request via API Gateway and write a record to DynamoDB.
• Process a file uploaded to S3—resize an image, parse a CSV, or trigger a downstream workflow.
• React to an SNS notification and send an email via SES.
• Stream records from a DynamoDB or Kinesis stream for real-time processing.

Supported Event Sources

Lambda integrates natively with a wide range of AWS services as event sources, including:

• Amazon API Gateway — trigger functions via HTTP/HTTPS requests.
• Amazon S3 — react to object creation, deletion, or modification events.
• Amazon DynamoDB Streams — process table change records in near real time.
• Amazon SNS and SQS — handle messages and queue-based workloads.
• Amazon EventBridge — scheduled or rule-based invocations.
• AWS Step Functions — orchestrate Lambda functions as steps in a workflow.

The full list of supported event sources is maintained in the AWS Lambda documentation.

Supported Runtimes

Lambda natively supports Node.js, Python, Java, Go, Ruby, C#, and PowerShell. For languages or runtimes not on this list, you can use a custom runtime via the Lambda Runtime API, which allows you to run virtually any language on top of the underlying Amazon Linux environment.

Limitations of AWS Lambda

Lambda’s constraints are not arbitrary—they are fundamental to how the service achieves elasticity and cost efficiency. Understanding them is essential before committing to a serverless architecture.

Maximum Execution Timeout

A Lambda function can run for a maximum of 900 seconds (15 minutes). Any workload that requires longer execution—large batch jobs, video transcoding, complex machine learning inference—must be decomposed or offloaded. Common approaches include:

• Job splitting — break long-running work into chunks, each handled by a separate invocation.
• Recursive invocation — the function re-invokes itself with state about what work remains.
• Step Functions orchestration — use AWS Step Functions to coordinate multiple Lambda executions as a workflow.
• Dedicated compute — fall back to EC2, ECS, or AWS Batch for genuinely long-running processes.

Ephemeral, Stateless Execution Containers

Lambda functions run in ephemeral containers that AWS provisions and destroys as needed. You must never rely on local state persisting between invocations. A warm container may be reused for a subsequent invocation (allowing /tmp caching as an optimization), but you cannot depend on this behavior.

Each execution environment has 512 MB of writable space in the /tmp directory. This is intended only for temporary files needed within a single invocation—not as durable storage. All persistent state must be stored in an external service such as DynamoDB, S3, ElastiCache, or RDS.

Binary and Native Dependency Compilation

Because Lambda runs on Amazon Linux, any native binaries or compiled extensions bundled with your function must be built for that environment. A binary compiled on macOS or Windows will not work. Use Docker with an Amazon Linux base image (AWS provides public.ecr.aws/lambda/* images for this purpose) to compile dependencies before packaging your deployment artifact.

Cold Starts

When Lambda needs to provision a new execution container—either because no warm container is available or because concurrency has scaled out—there is an initialization latency known as a cold start. Cold start duration varies by runtime, package size, and VPC configuration. For latency-sensitive workloads, strategies to mitigate cold starts include:

• Using Provisioned Concurrency to keep a set number of execution environments initialized and ready.
• Keeping deployment packages small to reduce initialization time.
• Choosing a runtime with lower cold-start overhead (Node.js and Python generally initialize faster than Java-based runtimes under equivalent conditions).

Concurrency and Throttling

Lambda scales by running multiple concurrent function instances. However, each AWS account has a default regional concurrency limit. Exceeding this limit causes throttling. If your workload can generate very high burst traffic, verify your account’s concurrency quota and request an increase via the AWS Service Quotas console before going to production.

When Serverless with AWS Lambda Makes Sense

Lambda is a strong fit when:

• Traffic is unpredictable or bursty and you want to avoid paying for idle capacity.
• Individual units of work are discrete, short-lived, and can be modeled as event-driven functions.
• Your team wants to minimize operational overhead and focus on application logic.
• You are building event-driven pipelines: file processing, stream processing, webhook handlers, or scheduled tasks.

Lambda is a poor fit when:

• Workloads run continuously or require execution times longer than 15 minutes.
• Latency requirements are extremely tight and cold starts cannot be tolerated even with Provisioned Concurrency.
• You need persistent local state or low-level OS control.
• Existing applications are tightly coupled to long-lived server processes.

Watch: Getting Started with AWS Lambda

Summary

Serverless computing—and AWS Lambda in particular—represents a genuine shift in how applications are built and operated. By delegating infrastructure management entirely to AWS, teams can ship features faster and pay only for the compute they actually use. However, Lambda’s constraints around execution time, statelessness, cold starts, and native dependencies require deliberate architectural decisions. Understanding these trade-offs upfront is the difference between a successful serverless migration and a painful one.

If one seeks evidence of how hot DevOps is these days, look to demand for appropriate skills as an indicator. And DevOps skills top the list these days.

That’s the word from a survey of 250 IT managers from OpsRamp, which finds widespread demand for DevOps capabilities and skills. For starters, nearly two-thirds are actively seeking a DevOps approach. 

DevOps skills are more in demand than any other IT-related skill, even surpassing cloud and data science skills. Close to two-thirds of respondents, 64%, indicated that DevOps is the most sought-after skill, above cloud certifications, machine learning, and even industry knowledge.  

Here are the top skills in demand cited in the survey:

1. DevOps 64% 
2. Cloud certifications 61% 
3. Industry domain knowledge 56% 
4. Development 50% 
5. Data science 47% 
6. Machine learning 44% 

The key is agility and ability to rapidly and predictably delivery functionality. Perhaps the survey report’s authors even understate the challenge when they note that “modern infrastructure is dynamic, ephemeral, elastic, and software-defined. Organizations today need technology practitioners who can not only maintain the health and performance of legacy workloads but also deliver agile and responsive operations for digital business.” 

Overall, finding people with the right combinations of skills is a challenge. Most enterprises, 94%, are finding it “somewhat difficult” to recruit candidates with the right technology and business skills. This is made more acute with the growing need to “significantly rethink and overhaul traditional IT operations practices,” brought on with the emergence of cloud platforms with microservices and containers, the survey report’s authors point out. “Digital operations teams will need to build the right skills, knowledge, and capabilities to thrive in a world where the technology infrastructure will be increasingly hybrid and new workloads will be largely invisible.”  

To adjust to a DevOps world, the survey’s authors make the following recommendations:

Embrace DevOps:  But of course. The catch is many organizations think they have DevOps underway, since they figured out how to get developers and IT operations teams talking to each other. That’s only the first step. “The most important skill for modern IT operations teams today is to incorporate DevOps practices — agile planning, configuration management, continuous integration, and automated deployments — into their organizational workflows for better teamwork, greater innovation, and faster customer feedback.”

Pay attention to technical infrastructure debt. There’s been plenty said and written about technical debt, in which accumulated fixes and workarounds get unwieldy. This can get in the way of DevOps flow. “Accumulated infrastructure debt can lead to frequent service disruptions, more manual operations, and lower quality of services. IT executives should retire infrastructure debt by eliminating constant sources of rework and betting on modern infrastructure platforms and tools.” 

Employ the right operational metrics. It’s good to stay on top of uptime, transaction throughput, power usage, and so on. But the business wants to know something else. “Pick the metrics that matter for business effectiveness, instead of highlighting KPIs that solely focus on infrastructure health. Operational performance metrics tied to organizational goals like customer churn, speed to market, or cost reduction make the case for IT’s relevance to the business.” 

Create a fantastic work culture. This point may be blindingly obvious, but also perhaps may be the hardest task of all. IT leaders don’t necessarily have the influence over the C-suite they need to make changes in management styles, culture, and — very importantly — incentive structures. The OpsRamp authors encourage managers and executives to “build a positive and friendly organizational culture to hire and retain the talent you need to win in a disruptive market. Companies should pay employees competitive salaries, provide flexibility in picking their technology stack, offer challenging business problems to work on, and sponsor attendance at industry events.” 

Overview

Splunk’s annual .conf19 conference drew more than 11,000 attendees to Las Vegas, including over 2,200 partners. The event served as the primary stage for Splunk to announce new products, share its platform roadmap, and articulate a unifying vision: moving customers beyond passive data collection toward active, data-driven decision-making. Splunk described this shift under the banner of its Data-to-Everything platform and the theme of turning data into doing.

The Tuesday morning keynote was particularly dense with announcements spanning cloud authorization, security operations, AIOps, stream processing, and social impact investment. Below is a breakdown of the major items.

Key Announcements at .conf19

Zonehaven Investment — Splunk Ventures Social Impact Fund

Splunk announced the inaugural investment from its newly launched Splunk Ventures Social Impact Fund, a $50 million fund targeting early-stage organizations that use data-driven approaches to create measurable social impact. The first portfolio company was Zonehaven, a platform designed to give fire departments and emergency response agencies real-time situational awareness during wildfire events.

Zonehaven combines intelligent evacuation zone management, advanced fire behavior modeling, real-time weather data, and continuous fire sensing. The timing of the announcement was notable: at the time of the conference, thousands of residents in California were being ordered to evacuate due to active wildfires, underscoring the operational relevance of the technology.

FedRAMP Authorization for Splunk Cloud

Splunk revealed that Splunk Cloud had received FedRAMP authorization from the General Services Administration (GSA) FedRAMP Program Management Office (PMO). FedRAMP (Federal Risk and Authorization Management Program) is the US government’s standardized security assessment framework for cloud services.

Achieving this authorization opens Splunk Cloud to federal agencies and their partners, enabling them to apply Splunk’s analytics capabilities to IT operations, security monitoring, and IoT use cases while meeting federal compliance requirements. For agencies that previously had to rely solely on on-premises Splunk Enterprise deployments, FedRAMP authorization removes a significant barrier to cloud adoption.

Enhanced IT Operations Portfolio

Splunk announced updates to its IT operations product line aimed at expanding its AIOps capabilities and supporting organizations at different stages of cloud migration. Key updates included:

• Splunk IT Service Intelligence (ITSI) — updated to improve service monitoring and anomaly detection using machine learning.
• Splunk App for Infrastructure (SAI) — updated to provide deeper visibility into hybrid infrastructure environments.
• Integrations with Splunk VictorOps and Splunk Phantom — tighter connections between alerting, incident response automation, and on-call management to reduce mean time to resolution (MTTR).

Together, these enhancements extend Splunk’s observability portfolio, allowing teams to correlate infrastructure metrics, log data, and service health in a single workflow.

Splunk Mission Control and the Security Operations Suite

One of the most significant security announcements was the launch of Splunk Mission Control as part of the new Splunk Security Operations Suite. Mission Control is a cloud-based solution that unifies three previously separate security products into a single analyst interface:

• Splunk Enterprise Security (ES) — the SIEM component for threat detection and investigation.
• Splunk Phantom — the SOAR (Security Orchestration, Automation and Response) component for automated playbooks and response actions.
• Splunk User Behavior Analytics (UBA) — the UEBA component for detecting insider threats and anomalous user activity.

By bringing SIEM, SOAR, and UEBA into one unified experience, Mission Control aims to reduce analyst context-switching and provide end-to-end visibility across the full threat detection, investigation, and response lifecycle. Security teams managing complex, high-volume alert environments stand to benefit most from this consolidation.

Increased Scale and Speed: Enterprise 8.0, DFS, and DSP

Splunk announced the general availability of three products focused on scaling data ingestion, search, and processing:

• Splunk Enterprise 8.0 — the latest major release of the flagship on-premises platform, bringing performance and usability improvements.
• Splunk Data Fabric Search (DFS) — a capability that federates search queries across multiple, heterogeneous data stores, presenting results in a single unified view. DFS is designed to eliminate the need to move or consolidate data before analyzing it.
• Splunk Data Stream Processor (DSP) — a real-time stream processing solution that continuously ingests high-velocity, high-volume data from multiple sources, applies transformations or enrichments in flight, and routes results to Splunk or third-party destinations.

Together, DFS and DSP address two common pain points for large-scale Splunk customers: the difficulty of querying data that is spread across disparate systems, and the latency involved in batch-loading data before it can be searched.

The Vision: From Data Collection to Data Action

Prior to the conference, Forbes spoke with Tim Tully, then Senior Vice President and Chief Technology Officer at Splunk, about the company’s long-term direction. Tully’s perspective offers useful context for understanding why Splunk framed .conf19 around the idea of turning data into doing.

Tully observed that data volume is no longer the core problem for most organizations. Enterprises today collect enormous quantities of data from a growing variety of devices, services, and platforms. The harder challenge is correlation and aggregation: stitching together data from disparate sources in a way that makes downstream analysis tractable. He argued that too many teams spend the majority of their effort understanding where data comes from rather than what to do with it — always looking backward at data lineage instead of forward at actionable insight.

The solution Splunk is pursuing centers on what Tully called unbounded learning: the ability to apply machine learning to unstructured, unsupervised streaming data without requiring data scientists to build and train models in advance. This approach is intended to democratize analytics, making it possible for teams without dedicated ML expertise to benefit from intelligent pattern detection and anomaly identification.

Tully also pushed back on what he described as the industry habit of using machine learning as a marketing differentiator. His position: customers are not buying machine learning as a feature. They are buying faster, better outcomes. The value of ML is invisible when it works well — it simply delivers results that would otherwise take analysts much longer to reach. That framing shaped how Splunk positioned its AIOps and security analytics announcements at .conf19.

Summary

Splunk’s .conf19 was a product-dense event with announcements spanning security operations, cloud compliance, stream processing, IT observability, and social impact. The connective thread across all of them was Splunk’s ambition to reduce the distance between raw data and actionable decisions — whether that means unifying a security analyst’s workflow in Mission Control, enabling real-time stream processing with DSP, or applying machine learning without manual model building. For DevOps and platform engineering teams evaluating Splunk’s roadmap, the move toward federated search (DFS) and real-time processing (DSP) alongside tighter AIOps integration represents the most operationally significant direction from the conference.

In this special guest feature, Ryohei Fujimaki, Ph.D., Founder and CEO of dotData, discusses how AI and ML are having a profound impact on enterprise digital transformation becoming crucial as a competitive advantage and even for survival. As the field grows, four trends emerge, shaping data science in the next five years. dotData is a spin-off of NEC Corporation and the first company focused on delivering full-cycle data science automation for the enterprise. Dr. Fujimaki is a world-renowned data scientist and was the youngest research fellow appointed in the 119-year history of NEC.

According to the Gartner Group, digital business reached a tipping point last year, with 49% of CIOs reporting that their enterprises have already changed their business models or are in the process of doing so. When Gartner asked CIOs and IT leaders which technologies they expect to be most disruptive, artificial intelligence (AI) was the top-mentioned technology.

AI and ML are having a profound impact on enterprise digital transformation becoming crucial as a competitive advantage and even for survival. As the field grows, four trends emerge, shaping data science in the next five years:

Accelerate The Full Data Science Life-Cycle

The pressure to grow ROI from AI and ML initiatives has pushed demand for new innovative solutions that accelerate AI and data science. Although data science processes are iterative and highly manual, more than 40% of data science tasks are expected to be automated by 2020, according to Gartner, resulting in increased productivity and broader usage of data across the enterprise.

Recently, automated machine learning (AutoML) has become one of the fastest-growing technologies for data science. Machine learning, however,  typically accounts for only 10-20% of the entire data science process. Real pains exist before the machine learning stage with data and feature engineering.  The new concept of data science automation goes beyond machine learning automation, including data preparation, feature engineering, machine learning, and the production of full data science pipelines. With data science automation, enterprises can genuinely accelerate AI and ML initiatives.

Leverage Existing Resources for Democratization

Despite substantial investments in data science across many industries, the scarcity of data science skills and resources often limits the advancement of AI and ML projects in organizations.  The shortage of data scientists has created a challenge for anyone implementing AI and ML initiatives, forcing a closer look at how to build and leverage data science resources.

Other than the need for highly specialized technical skills and mathematical aptitude, data scientists must also couple these skills with domain/industry knowledge that is relevant to a specific business area. Domain knowledge is required for problem definition and result validation and is a crucial enabler to deliver business value from data science. Relying on “data science unicorns” that have all these skill sets is neither realistic nor scalable.

Enterprises are focusing on repurposing existing resources as “citizen” data scientists. The rise of AutoML and data science automation can unlock data science to a broader user base and allow the practice to scale. By empowering citizen data scientists allowing them to execute standard use cases, skilled data scientists can focus on high-impact, technically-challenging projects to produce higher values.

Augment Insights for Greater Transparency

As more organizations are adopting data science in their business process, relying on AI-derived recommendations that lack transparency is becoming problematic. Increased regulatory oversight like the GDPR has exacerbated the problem. Transparent insights make AI models more “oversight” friendly and have the added benefit of being far more actionable.

White-box AI models help organizations maintain accountability in data-driven decisions and allow them to live within the boundaries of regulations. The challenge is the need for high-quality and transparent inputs (aka “features”), often requiring multiple manual iterations to achieve the needed transparency. Data science automation allows data scientists to explore millions of hypotheses and augments their ability to discover transparent and predictive features as business insights.

Operationalize Data Science in Business

Although ML models are often tiny pieces of code, when models are finally deemed ready for production, deploying them can be complicated and problematic. For example, since data scientists are not software engineers, the quality of their code may not be production-ready. Data scientists often validate the models with down-sampled datasets in labs environments and models may not be scalable enough for production-scale datasets. Also, the performance of deployed models decreases as data invariably changes, making model maintenance pivotal to extract business value from AI and ML models continuously. Data and feature pipelines are much bigger and more complex than ML models themselves, and operationalizing data and feature pipelines is even more complicated.  One of the promising approaches is to leverage concepts from continuous deployment through APIs. Data science automation can generate APIs to execute the full data science pipeline, accelerating deployments while also providing an ongoing connection to development systems to accelerate the optimization and maintenance of models.

Data science is at the heart of AI and ML. While the promise of AI is real, the problems associated with data science are also real. Through better planning, closer cooperation with line of business and by automating the more tedious and repetitive parts of the process, data scientists can finally begin to focus on what to solve, rather than how to solve.

Why Deep Learning Workloads Demand Better Orchestration

Training deep learning models at scale is not a trivial infrastructure problem. The software stack alone — frameworks, CUDA libraries, Python dependencies, custom pre-processing pipelines — is complex enough to make consistent deployment across even a handful of servers a significant operational burden. Scale that to dozens or hundreds of nodes in a high-performance computing (HPC) environment, and the problem compounds quickly.

At the Dell EMC HPC and AI Innovation Lab, engineers tackled this problem by adopting Kubernetes as the orchestration layer for their deep learning workloads. The result: faster training cycles, more reproducible environments, and a cleaner path from research to production.

What Containers Actually Solve

Before understanding why Kubernetes matters here, it helps to be precise about what containers provide in an AI/ML context.

A container packages a software application together with everything it needs to run: a virtualized operating system layer, system libraries, runtime dependencies, and application code. For a deep learning environment, the container image also bundles the chosen framework — TensorFlow, PyTorch, or similar — along with all associated libraries and configuration.

The key benefit is environment portability. Once a container image is built and validated on a single development machine, it can be deployed identically to any number of host systems without manually replicating OS configurations, driver versions, or package installs. This eliminates a whole class of “works on my machine” failures that plague distributed ML training.

Kubernetes as the Orchestration Layer

Containers solve the packaging problem. Kubernetes solves the management problem: how do you schedule, scale, monitor, and recover containerized workloads across a large fleet of nodes?

Kubernetes is an open-source container orchestration platform originally developed by Google and now maintained under the Cloud Native Computing Foundation (CNCF). Its core responsibilities include:

• Scheduling: Deciding which node in a cluster runs which container, based on available resources and defined constraints.
• Scaling: Automatically increasing or decreasing the number of running container instances in response to workload demand.
• Self-healing: Restarting failed containers, rescheduling workloads away from unhealthy nodes, and maintaining declared state.
• Service discovery and load balancing: Exposing containers to each other and to external clients without manual network configuration.
• Configuration management: Injecting secrets, environment variables, and config maps into containers at runtime.

For AI teams, Kubernetes also provides a familiar abstraction over hardware heterogeneity. Whether a node has GPUs, FPGAs, or only CPUs, resource requests and limits in Kubernetes manifests let schedulers place training jobs on the right hardware automatically.

Kubernetes in an HPC Deep Learning Environment

The Dell EMC HPC and AI Innovation Lab applied Kubernetes to their Zenith and Rattler supercomputing clusters, which span thousands of CPU and GPU/accelerator nodes. Their workflow demonstrates a practical pattern other teams can replicate:

1. Develop locally in a container: Build and validate the entire deep learning environment — framework, dependencies, model code — on a single development system inside a container.
2. Push the container image to a registry: Store the validated image in a container registry accessible to all cluster nodes.
3. Distribute via Kubernetes: Use Kubernetes to pull and run the image across target nodes, ensuring every node runs an identical environment.
4. Schedule training jobs: Use Kubernetes job scheduling to orchestrate distributed training across many processors, replacing the role that traditional HPC batch schedulers (like SLURM or PBS) previously played.
5. Monitor and iterate: Use Kubernetes-native tooling or integrated observability stacks to track job status, resource utilization, and failure events.

This approach materially accelerates throughput for training workloads and reduces the iteration time between experiments — a critical advantage when developing models for compute-intensive domains like natural language processing, image and video classification, and recommendation systems.

Tuning Infrastructure to the Workload

Running Kubernetes well in an HPC/AI context requires more than installing a cluster and submitting manifests. The infrastructure underneath needs deliberate tuning:

• Compute: GPU nodes need the appropriate device plugins installed so Kubernetes can schedule GPU-aware workloads correctly. CPU affinity and NUMA topology awareness matter for latency-sensitive jobs.
• Storage: Deep learning training requires high-throughput access to large datasets. Persistent volumes backed by parallel file systems or high-performance object storage must be properly configured and mounted into training pods.
• Networking: Distributed training relies on low-latency, high-bandwidth inter-node communication. In HPC environments, this often means integrating InfiniBand or high-speed Ethernet fabrics with Kubernetes networking, sometimes using RDMA-capable network plugins.

Getting these three layers right — compute, storage, network — is what separates a Kubernetes deployment that merely works from one that delivers real performance gains for AI workloads.

Open Source vs. Enterprise Kubernetes: Understanding the Trade-offs

One of the practical lessons from this kind of lab work is helping engineering and platform teams understand the difference between upstream Kubernetes and enterprise Kubernetes distributions.

Upstream Kubernetes gives you maximum control and the latest features, but requires your team to own cluster lifecycle management, security hardening, networking configuration, and upgrade testing.

Enterprise distributions (such as Red Hat OpenShift, Rancher, VMware Tanzu, or similar offerings) bundle additional tooling for multi-tenancy, role-based access control, integrated monitoring, and supported upgrade paths. They trade some flexibility for operational reliability and vendor support — often the right call for production AI platforms that data science teams depend on.

Neither choice is universally correct. The right answer depends on team size, operational maturity, security requirements, and how much of the Kubernetes control plane your team wants to own.

Why Kubernetes Has Become the De Facto Standard for AI Infrastructure

Kubernetes has emerged as the dominant platform for productionizing AI workloads for several compounding reasons:

• Portability across clouds and on-premises infrastructure means teams are not locked into a single vendor’s managed training service.
• Declarative configuration makes cluster state reproducible and auditable, which matters for regulated industries and compliance-sensitive AI use cases.
• A rich ecosystem of AI-specific extensions — Kubeflow for ML pipelines, Volcano for HPC-style batch scheduling, KServe for model serving, and others — has grown up around Kubernetes, making it a platform rather than just an orchestrator.
• Community scale means bugs are found and fixed quickly, security vulnerabilities are disclosed and patched on a known cadence, and operational knowledge is widely available.

Key Takeaways for DevOps and Platform Engineers

If your organization is evaluating or expanding Kubernetes use for AI and ML workloads, the experience from the Dell EMC HPC and AI Innovation Lab points to a few durable principles:

• Start with the container, not the cluster. Get your deep learning environment fully containerized and reproducible before tackling multi-node orchestration.
• Treat infrastructure tuning as a first-class concern. Compute, storage, and networking configuration for AI workloads requires deliberate engineering — defaults are rarely sufficient.
• Evaluate open source vs. enterprise Kubernetes honestly. Operational overhead is real; factor it into the decision alongside feature requirements.
• Use the ecosystem. Tools like Kubeflow, Volcano, and KServe exist precisely to fill the gaps between vanilla Kubernetes and production ML operations needs.
• Plan for scale from the start. Kubernetes shines when workloads grow. Designing your manifests, storage claims, and network policies with scale in mind avoids painful refactoring later.

Done right, Kubernetes is not just an operational convenience for AI teams — it becomes a genuine accelerant for the time between idea and production-quality model.

What Problem Does LazyDocker Solve?

Docker’s CLI is powerful, but day-to-day container management often means juggling multiple terminal sessions, remembering long command strings, and manually piecing together logs and resource metrics. LazyDocker consolidates all of this into a single, always-visible terminal window.

As the project creator describes in the GitHub README:

“Memorising docker commands is hard. Memorising aliases is slightly less hard. Keeping track of your containers across multiple terminal windows is near impossible. What if you had all the information you needed in one terminal window with every common command living one keypress away (and the ability to add custom commands as well). Lazydocker’s goal is to make that dream a reality.”

Key Features

• Full keyboard and mouse support — navigate the UI without leaving the terminal; keybindings are viewable and remappable.
• Custom commands and key rebinding — extend LazyDocker beyond its defaults by defining your own commands in the config file.
• Real-time metrics with ASCII graphs — visualize CPU and memory usage for containers directly in the terminal.
• Log viewer — tail logs for any container or Docker Compose service without a separate terminal window.
• Container lifecycle management — restart, stop, remove, or rebuild containers and services with a single keypress.
• Disk space cleanup — identify and remove containers consuming unnecessary disk space.
• Image layer inspection — view ancestor layers of Docker images.
• Customizable colors and high-contrast mode — adapt the UI to your terminal theme.
• Docker Compose support — manage multi-container applications alongside standalone containers in the same interface.

Prerequisites and Installation

To run LazyDocker, you need:

• Docker — the project recommends running the latest stable version.
• Docker Compose v1.23.2 or newer (optional, required only if you manage Compose projects).
• Go 1.8 or newer (required if building from source).

LazyDocker can be installed in several ways:

• Binary release — download a pre-built binary directly from the GitHub releases page.
• Homebrew (macOS/Linux) — install via brew install lazydocker.
• AUR (Arch Linux) — available through an AUR package manager such as yay or paru.
• Go install — build from source using go install github.com/jesseduffield/lazydocker@latest (syntax may vary by Go version).

Configuration

LazyDocker is customizable through a YAML configuration file. Options include defining custom commands, adjusting graph display settings, changing color themes, and remapping keyboard shortcuts. Refer to the official configuration documentation on GitHub for a full reference of available fields.

See It in Action

Jesse Duffield published a demo video walking through LazyDocker’s interface and core features. Watching it is the quickest way to judge whether the tool fits your workflow before installing it.

Contributing and Related Projects

LazyDocker is an active open-source project. If you encounter a bug or want a feature added, the project welcomes issues and pull requests on GitHub at github.com/jesseduffield/lazydocker.

If you find the terminal-UI approach useful for Docker, Jesse Duffield’s LazyGit applies the same philosophy to Git. It is a terminal UI for git commands, also written in Go, and available at github.com/jesseduffield/lazygit.

Is LazyDocker Right for You?

LazyDocker is best suited for developers and DevOps engineers who:

• Work primarily in the terminal and prefer not to switch to a browser-based UI such as Portainer.
• Manage multiple containers or Compose stacks simultaneously and want a consolidated view.
• Want faster access to logs and metrics without remembering exact CLI flags.
• Are comfortable with keyboard-driven workflows.

If your team already uses Docker Desktop’s built-in GUI or a full orchestration platform like Kubernetes with a dedicated dashboard, LazyDocker’s value proposition is narrower — but for local development and single-host Docker environments, it is a lightweight and fast alternative.

Why CI/CD Matters for Machine Learning Teams

Continuous integration and continuous deployment (CI/CD) are well-established practices in software engineering, but their value extends well beyond traditional application development. As data science and machine learning (ML) projects grow in organizational importance, the same discipline that makes software teams productive — frequent commits, automated testing, and reliable deployment pipelines — becomes essential for teams building and maintaining ML models.

ML projects face a unique kind of complexity: practitioners must blend advanced statistics, mathematical modelling, and software engineering, all while operating under real business constraints such as deadlines, limited compute budgets, and shifting requirements. CI/CD practices provide a structural framework that helps teams manage this complexity without sacrificing speed or quality.

Defining CI/CD in Plain Terms

Continuous Integration (CI)

Continuous integration is the practice of merging code changes into a shared repository frequently — often multiple times per day. Each merge triggers an automated build and test suite, surfacing integration problems early when they are cheapest to fix. In a data science context, CI checks can validate not just code syntax but also data schemas, feature engineering logic, and model training scripts.

Continuous Deployment (CD)

Continuous deployment extends CI by automatically building, testing, and preparing software for release. Teams that adopt CD can push updates to production reliably and repeatedly, reducing the risk associated with large, infrequent releases. For ML teams, CD pipelines can automate model retraining, evaluation against a holdout dataset, and promotion to a serving endpoint — all without manual intervention.

How Microservices Architecture Enables CI/CD for ML

Modern software is increasingly delivered as microservices — small, independently deployable services that communicate over well-defined APIs. Microservice architectures allow one component to be updated, tested, and released without disrupting other parts of the system. This property is particularly valuable for ML-driven products, where a model serving endpoint may need to be updated independently of the user-facing application that consumes its predictions.

Microservices also make it easier to keep data-related concerns — such as privacy compliance updates or schema migrations — decoupled from model logic. A pipeline can apply a data transformation change and validate downstream model behaviour in an isolated environment before promoting that change to production.

Reducing Technical Debt in Data Projects

Technical debt — the long-term cost of shortcuts taken during development — is a serious risk in ML projects. Common examples include:

• Undocumented data preprocessing steps that are difficult to reproduce.
• Hard-coded file paths or credentials that break across environments.
• Model training scripts that have never been tested outside a single analyst’s laptop.
• Missing version pinning for libraries and data dependencies.

Organizations that operate multiple deployment environments — development, staging, and production — are especially vulnerable. A CI/CD pipeline enforces consistency across these environments by codifying environment setup, dependency installation, and validation steps. This directly addresses one of the most common ML operationalization failures: a model that works locally but cannot be reliably reproduced or deployed elsewhere.

Where Managers and Project Leads Fit In

CI/CD is not purely a technical concern. Project managers and business stakeholders can contribute meaningfully to a CI/CD workflow, particularly through user acceptance testing (UAT).

UAT is a test phase that evaluates whether a system meets user needs and business requirements before it is released. In an ML context, UAT might involve checking that a recommendation model’s outputs are coherent, that fairness constraints are satisfied, or that a forecasting model meets an agreed-upon accuracy threshold on business-relevant metrics. Managers can define these acceptance criteria, giving the test team clear, measurable parameters for evaluating model quality. When UAT is integrated into a CI/CD pipeline, every new model version is automatically validated against those criteria before it can advance toward production.

This approach reduces costly late-stage discoveries and creates a documented audit trail of how and why model versions were approved or rejected.

Tooling Considerations for ML CI/CD Pipelines

Selecting the right tools is an important step when establishing a CI/CD workflow for machine learning. Several areas deserve attention:

• Version control: All code, configuration files, and — where feasible — data transformation scripts should be stored in a version control system such as Git. This ensures a documented history of every change that influences a model’s behaviour.
• Dependency management: Whether a team works in Python, R, or another language, package versions should be pinned and tracked. Tools like pip-tools, conda, or renv (for R) help maintain reproducible environments.
• Pipeline orchestration: Tools designed for ML workflows — such as those offered by major cloud providers or open-source orchestration platforms — can automate training runs, evaluations, and deployments as part of a broader CI/CD pipeline.
• Model registry: A model registry records which model version is deployed to which environment, making it straightforward to roll back if a new version introduces a regression.
• Monitoring: Deployment is not the end of the pipeline. Automated monitoring for data drift and model performance degradation closes the feedback loop and can trigger retraining pipelines when thresholds are breached.

Major cloud platforms have invested heavily in managed services that address these needs, including faster model training, improved workflow management, and integrated security for model deployment. Open-source tooling has grown substantially in this space as well, giving teams options regardless of infrastructure preference.

Aligning CI/CD Cadence with ML Training Time

One practical tension in ML CI/CD is the time required to train a model. A typical software CI pipeline runs in minutes; a deep learning training job may run for hours or days. Teams need to design their pipelines with this asymmetry in mind. Common strategies include:

• Running fast smoke tests (on a small data sample) on every commit, reserving full training runs for scheduled jobs or significant code changes.
• Caching intermediate artefacts — such as preprocessed datasets or partially trained model checkpoints — to avoid redundant computation.
• Separating the code CI pipeline (fast, runs frequently) from the model training pipeline (slower, triggered by data or model code changes).

Thoughtful pipeline design ensures that the CI/CD process accelerates delivery rather than becoming a bottleneck of its own.

From Data to Business Decision

Translating raw data into a reliable, repeatable business decision is difficult. The gap between a promising notebook prototype and a production model that teams trust enough to act on is where many ML projects stall. CI/CD practices — version control, automated testing, consistent environments, and systematic deployment — directly address the failure modes that cause that gap.

As ML becomes embedded in more products, services, and software-managed devices, the discipline of CI/CD is no longer optional for data teams. Organisations that apply these practices early build a foundation for iterating on models safely, maintaining them at scale, and aligning their outputs with the business requirements that make them valuable.