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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If you have been working from home during the recent lockdowns, you probably don’t feel like you are making many new connections, but when we come to look back on this time in a few years, it might prove to have been one of the most effective networking sessions of the past decade.

Nowhere is this more true than in the twin worlds of big data and DevOps. It seems that being forced to work from home, far from locking engineers in both disciplines deeper within their silos, has brought them out of their shells. In recent months, we’ve seen an outpouring of speculation on how DevOps and big data can work together, and even a number of impassioned manifestos on why it is time to bring DevOps ideas to big data.

In this article, we’ll look at why this crossover is suddenly so popular, how these two disciplines can engage in a symbiotic relationship with each other, and how they are already doing so.

The Pandemic and the Silo
For consumers, the experience of lockdown has accelerated internet usage trends. While some of the changes that have recently swept the online world were easy to guess, the speed at which they have taken hold has been astounding. It’s therefore no exaggeration to say, as the New York Times put it recently, that the virus has changed the way we use the internet.

These consumer trends are driving changes throughout the software development industry, forcing firms to adapt to rapid release, testing and update cycles.

One of the biggest changes in the way that developers collect data over the past decade has been the source of the data. This year, and largely due to the COVID-19 pandemic, we passed a notable watershed: Mobile devices and smartphones now account for more than half of all internet traffic, resulting in an increased data flow that boggles the mind and creates issues of its own. This increased data load from consumer mobile devices, rather than from corporate data acquisition systems, is forcing teams to work more closely together.

In the words of Bill Detwiler, editor in chief of TechRepublic, in a recent CBS interview: “The COVID-19 pandemic has accelerated the blending of data analytics and DevOps, meaning developers, data scientists, and product managers will need to work more closely together than ever before.” For most firms, the way in which these consumer trends are fed into the development life cycle is via analytics teams. This has meant that, during the pandemic, analytics teams were generating actionable insights at an increased rate, and this has been the driving force behind the newly intimate relationship between big data and DevOps staff.

Waterfall vs. DevOps
To understand the way in which big data and DevOps teams can best work together, it’s instructive to visualize the way in which developers, operational staff and data analytics agents work.

A decade ago, most software developers were using some form of waterfall development methodology. As the name suggests, this approach was essentially linear—the specifications for software were set in advance, and after this point multiple teams worked in tandem to deliver a continuous “stream” of software updates.

The DevOps framework changed that by encouraging communication and collaboration between operations and development staff. The idea, in short, was to ensure that software underwent continual testing—not just in terms of usability and security, but also against the requirements of the operations staff who were charged with maintaining it.

Now, we might be facing a shift of similar scale. Many firms have now adopted big data frameworks, but these systems generally have been seen as a way of harvesting and collating market insights. This was always a waste of potential for big data because as long as it was regarded as part of marketing, it’s utility in other parts of an organization were difficult to discern.

Not any longer, it seems. The changes caused by the pandemic—both in consumer behavior and in the way that organizations communicate internally—have given developers an appreciation of the value of the data that analytics teams hold.

Pioneers Show the Way Forward
It’s also worth noting that big data and DevOps teams working in tandem is not a utopian vision; rather, this way of working was already being explored within leading companies even before the pandemic.

In retrospect, in fact, it seems crazy that these two teams ever worked independently. If you’re a transportation company, the ability to track your loads on the road and the health and safety of the cargo that they’re carrying has become mission-critical. If you’re in the armed forces and using drones on the battlefield to conduct and report reconnaissance in real-time flyovers, the ability to adapt software parameters on-the-fly can be the difference between success and failure.

The military is, in fact, the place to look for examples of how big data and DevOps will shortly become one process. Big data in the military has long been more advanced than that in the civilian sector and is beginning to inform everything from IT predictive analytics to real-time usage analytics.

The Future of Big Data and DevOps
There remain, of course, significant challenges to be overcome before the promise of DevOps working with big data teams can be realized. Perhaps the most thorny of these is the fact that computing performance is crucial for big data in a way it hasn’t been for development: The latter teams may need to build capabilities to make use of the insights generated by big data teams.

As a very unique year slowly comes to an end, experts are here again to predict the most promising trends in the app development industry. In this article, we will discuss the cutting-edge software technologies, programming languages and other areas that I believe are going to gain more power in the software industry battle next year

1. Native App Development

It’s no surprise that this approach will gain momentum, especially in the banking/fintech industry, multimedia products and other areas with increased demand for flawless performance on mobile devices, reliability, quality and security. Native development provides enhanced user experience and outstanding performance when these parameters are a priority.

2. The Internet Of Things

The rise of IoT is not going to stop in 2021. Smart home systems, Bluetooth trackers, car gadgets, point-of-sales beacons, IoT devices for manufacturers and plants, smart wearables for healthcare and fitness. This is just a handful of possible use cases for internet-of-things technologies. IoT should be mixed with AI and big data for maximum productiveness, but it remains highly trendy. According to a 2018 Bain and Co. report, “We expect the markets for IoT hardware, software, systems integration, and data and telecom services to grow to $520 billion in 2021, more than double the $235 billion spent in 2017.”

There definitely will be more implementation opportunities that will help people improve their quality of life. More affordable and effective IoT devices will change most industries. As for IoT software development, I think IoT engineers from all over the world should improve their programming skills to write code for energy-efficient connected devices.

3. Human Augmentation

Human augmentation is a set of technologies that is used to enhance, replace or complement the natural abilities of people. Human 2.0 technologies exist in the form of implants built into the body, such as devices to restore sight or hearing, or external devices and tools, such as exoskeletons to improve natural capabilities. New technologies and use cases are on their way to significantly improving human lives, and the technology we’ve seen from things like Google Glass is just the beginning.

4. Big Data

Big data computation is still among the top trends and investment opportunities. In 2021, a significant part of the job is going to be carried out by the tools that process everything in memory right away instead of storing data after each calculation. Batch processing frameworks are going to be in demand, especially when it comes to social media data, documents and text analysis, and clickstream media. I believe big data will be used to facilitate climatic change research and search for new medical cures, and it will be used for a more natural language processing and easier data governance. However, cybersecurity in this area will remain a challenge.

5. Artificial Intelligence Expansion

Despite being a buzzword, used both correctly and incorrectly, AI is still a top technology and will become more powerful, affordable and widespread. Deep learning, machine learning, speech processing and more are going to be used more in ecommerce, healthcare, education and entertainment apps. AI is what helps businesses and organizations provide more personalized experiences for customers, and this trend will continue in a more cost-effective manner next year.

6. Continuous Integration And Deployment

This practice is aimed to cut down the cycle of feature delivery, make bug fixing more frequent and effective, and allow more flexibility and experiments in software development. Continuous delivery means apps get into production for thorough testing, while continuous deployment updates them automatically. As the code is added to a shared repository, it is tested so the delivery is accelerated. It enables software development teams to continuously improve functionality and deliver products faster. No wonder more software development companies will implement this approach in their work.

7. Progressive Web Apps

When comparing websites to web apps, the former ones are easier to access, while the latter can provide broader functionality and top performance due to embracing platform-specific hardware support. Still in their infancy, progressive web apps have become a valuable compromise between easy access and five-star performance. Users get a full-fledged and full-screen experience with less data usage and shorter loading time. They transfer the app-like features into the browser window, thus marking a revolution of web development. A lot of world-renowned platforms — including Forbes, AliExpress, Pinterest and Twitter — already make use of PWA adoption. To my mind, they are going to dominate the market of web products in 2021.

8. Low-Code Development

Previously, it took large, dedicated teams of software engineers and architects to create any type of digital solution. It used to be very expensive and barely available to medium-sized businesses. However, the low-code development trend is here to simplify complex programming languages and provide access to a coding miracle to more specialists. Low-code platforms have the necessary potential to revolutionize the software market, avoid a lot of manual work, and provide simple interfaces to help people build new apps at a low investment of money, time, and specific skills.

9. Cross-Platform And Hybrid Development

For some industries or application types, cross-platform development becomes a major time-saver and facilitator. So we cannot say that only the development will be mostly native in 2021. The common back-end code base for mobile platforms can be a compromise between functionality and time to market. Back-end code shareability with the native rendering engine (as in the case of cross-platform development) will achieve impressive results at a reasonable investment in the coming year.

Takeaway

I believe new programming languages will appear at the forefront of software engineering next year. You can also expect to see new technologies appear and some of the 2020 buzzwords vanish. Software development is a highly competitive industry that impacts every sector — it will only continue to grow and become more diversified.

Baidu Joins China’s ‘New Infrastructure’ Wave

Baidu, China’s dominant search engine and one of its most prominent AI-focused technology companies, announced ambitious long-term plans in June 2020 to significantly scale its investments in artificial intelligence, cloud computing, data centers, and semiconductor chips. The announcement positioned Baidu alongside Alibaba and Tencent, both of which had already unveiled their own commitments to what the Chinese government calls “new infrastructure.”

“New infrastructure” is a policy framework promoted by the Chinese government to accelerate the country’s technology-driven economic transformation. It encompasses investment in areas such as AI, the Internet of Things (IoT), big data, robotics, and 5G networks. The initiative gained added urgency as China sought to stimulate economic recovery following the disruption caused by the Covid-19 pandemic.

Key Targets in Baidu’s 10-Year Plan

Baidu’s announcement laid out several headline targets, though the company did not disclose specific investment figures. The key commitments included:

• 5 million intelligent cloud servers by 2030 — Baidu stated it aims to operate more than 5 million AI-capable cloud servers within the decade, aligning with China’s broader national goal of becoming a global AI leader by 2030.
• Supercomputer-scale computing power — By 2030, Baidu claimed its cloud infrastructure’s aggregate computing capability should be approximately seven times greater than the combined computing power of the world’s top 500 supercomputers at the time of the announcement. This is a forward-looking projection, not a current measurement.
• 5 million AI professionals trained in five years — Baidu committed to training 5 million AI experts over the following five years, building on its existing partnerships with more than 200 Chinese universities to develop deep learning and AI curricula.

AI Talent Development and the Education Push

The talent development target reflects a broader national effort to close the AI skills gap with the United States. According to a South China Morning Post report from March 2020 — citing a Chinese government document — China’s Ministry of Education approved 180 universities to establish new AI-related undergraduate programs, up sharply from just 35 institutions the previous year. Baidu’s own university partnership program sits within this wider ecosystem of state-encouraged AI education.

The urgency around building domestic AI talent has been amplified by tightening U.S. export controls and technology-transfer restrictions targeting Chinese companies, which have motivated Beijing to accelerate homegrown expertise and reduce reliance on foreign technology.

What ‘New Infrastructure’ Means in Practice

Understanding the “new infrastructure” policy label is important context for evaluating these announcements. Unlike traditional infrastructure stimulus — roads, bridges, railways — China’s new infrastructure push is explicitly oriented around digital and advanced-technology foundations. The key pillars include:

• Artificial Intelligence — National AI platforms, research labs, and enterprise AI services
• 5G networks — Accelerated rollout of next-generation wireless infrastructure
• Industrial Internet of Things (IIoT) — Connectivity and sensors embedded in manufacturing and logistics
• Big Data centers — Large-scale data storage and processing facilities
• Cloud computing — Scalable enterprise cloud services to support digital transformation
• Robotics and smart manufacturing — Automation investments across industrial sectors

For technology companies like Baidu, the policy creates both an opportunity and an expectation: major platforms are effectively encouraged — and in some cases expected — to align their capital spending with national infrastructure priorities.

Competitive Context: Alibaba, Tencent, and Baidu

Baidu’s announcement came after both Alibaba and Tencent had published their own new infrastructure investment plans. For Baidu, AI and cloud represent a critical strategic battleground. While Alibaba Cloud is the dominant player in China’s public cloud market, Baidu has differentiated itself with a focus on AI-native services — including its PaddlePaddle deep learning framework, autonomous driving platform (Apollo), and AI chip development efforts. The 10-year roadmap signals that Baidu intends to use the new infrastructure wave to consolidate and expand that AI identity at infrastructure scale.

Takeaways for DevOps and Cloud Engineering Teams

For engineering teams working in or alongside the Chinese cloud market, Baidu’s long-term commitments point to several practical implications:

• Expanding cloud capacity — A target of 5 million intelligent servers suggests significant growth in Baidu Cloud’s infrastructure footprint and, potentially, greater regional availability and competitive pricing over time.
• AI-integrated cloud services — Baidu’s roadmap prioritizes “intelligent” servers, implying that AI acceleration hardware (such as custom AI chips) will be increasingly embedded in its cloud offerings rather than offered only as specialist instances.
• Talent pipeline growth — A larger pool of AI-trained engineers in China will affect hiring, open-source contribution patterns, and the pace of AI adoption across the region’s enterprise sector.
• Chip sovereignty — Baidu’s stated investment in chips is part of a wider Chinese push for semiconductor self-sufficiency, which has direct implications for supply chain planning in global technology organizations.

These are long-horizon commitments announced in mid-2020. Progress against specific targets — particularly the server count and computing power claims — would require independent verification against current Baidu disclosures.

1. Post Graduate Certification in Big Data Analytics — E&ICT, IIT Guwahati

This programme is designed for graduates who want to build a career in Big Data Analytics, Data Science, or Machine Learning. It spans 11 subject areas including Statistics for Analytics, Data Analytics with Excel and SQL, Python, Java, Linux, Big Data and Hadoop, Apache Spark, MongoDB, Business Intelligence and Data Mining, and Tableau.

• Duration: 9 months
• Price: ₹2,25,000
• Eligibility: Graduates with a minimum of 50% marks in their undergraduate degree
• Certificate: Joint certificate from E&ICT IIT Guwahati and IBM upon completion

2. Machine Learning — Coursera

One of the most recognised introductory machine learning courses available online, this Coursera offering covers logistic regression, artificial neural networks, and a range of ML algorithms. The course balances theoretical foundations with practical implementation exercises, making it suitable for learners who want to apply ML techniques to real problems quickly.

• URL: coursera.org/learn/machine-learning
• Duration: Approximately 64 hours
• Price: ₹4,508 (pricing may vary; audit option may be free)
• Eligibility: Open to all learners
• Certificate: Electronic certificate, shareable on LinkedIn

3. Data Science Architect Master’s Course — Intellipaat

This master’s-level programme covers the full breadth of Data Science tooling: R, Hadoop, Apache Spark, Scala, Deep Learning, Tableau, SAS, SQL, and MongoDB. The curriculum includes 10 core courses, 53 industry projects, and one Capstone project. Learners also receive five IBM-co-created self-paced courses covering Deep Learning with TensorFlow, chatbot development with Watson Assistant, R for Data Science, Spark MLlib, and Python for Data Science.

• URL: intellipaat.com — Data Science Architect
• Duration: 232 hours of online instructor-led training
• Price: ₹62,000
• Eligibility: No prerequisites
• Certificate: IBM certificate upon completion

4. Deep Learning Explained — edX

This edX course provides a practical introduction to deep learning. Topics include multi-class classification using logistic regression, handwritten digit recognition with deep neural networks and convolutional networks, time-series forecasting with recurrent networks, and text processing using LSTM units. It is a good entry point for those with a data science background who want to move into neural network architectures.

• URL: edx.org — Deep Learning Explained
• Duration: 6 weeks
• Price: Free to audit; verified certificate costs $99
• Eligibility: Basic programming skills and working knowledge of data science recommended

5. Cloud and DevOps Architect Master’s Course — Intellipaat

This programme is directly relevant to DevOps engineers and cloud practitioners. It covers AWS, Azure, DevOps toolchains, Python, Java, Splunk, and Linux across 11 courses, 26 industry projects, and a Capstone project. Learners also receive official Microsoft course material on integrating on-premises identity infrastructure with Azure and implementing security in Azure development solutions.

• URL: intellipaat.com — Cloud & DevOps Architect
• Duration: 147 hours of online instructor-led training
• Price: ₹56,900
• Eligibility: No prerequisites
• Certificate: Joint IBM and Microsoft certificate upon completion

6. Mastering Python — Networking and Security — Udemy

Python is the de-facto scripting language for network automation and security tooling. This Udemy course focuses specifically on using Python for network scripting and security tasks. It covers script creation, threading, exception handling, and leveraging Python networking libraries to build practical automation scripts.

• URL: udemy.com — Mastering Python: Networking and Security
• Duration: Approximately 5 hours of on-demand video
• Price: ₹360 (Udemy prices fluctuate; check current listing)
• Eligibility: Basic programming awareness helpful but not strictly required

7. Artificial Intelligence Master’s Course — Intellipaat

This AI-focused master’s programme covers Data Science, Python, R, Statistics, Natural Language Processing, SAS, Excel-based analytics, and Tableau. It is designed for learners who want a broad AI and ML skill set without a strict programming prerequisite, though basic programming knowledge will help you progress faster.

• URL: intellipaat.com — Artificial Intelligence Master’s Course
• Duration: 166 hours of online instructor-led training
• Price: ₹46,000
• Eligibility: No prerequisites; basic programming knowledge is an advantage
• Certificate: IBM certificate upon completion

8. Web Development with Django and AngularJS — Pluralsight

This Pluralsight course teaches full-stack web development by combining Django on the back end with AngularJS on the front end. Students build a complete web application, learn to connect front-end JavaScript to back-end Python using REST and AJAX, and finish with a working, interactive application they can extend independently.

• URL: pluralsight.com — Django and AngularJS Web Development
• Duration: 3 hours 42 minutes
• Price: Included in Pluralsight subscription (approximately $19/month at time of writing; check current pricing)
• Eligibility: Open to all learners

9. Business Intelligence Architect Master’s Course — Intellipaat

For data professionals who work closer to the business layer, this BI-focused programme covers Informatica, Tableau, MSBI, Power BI, MS SQL, data warehousing and Erwin modelling, Azure Data Factory, and SQL DBA administration. The programme includes 9 core courses and 32 industry-based projects.

• URL: intellipaat.com — Business Intelligence Master’s Course
• Duration: 150 hours of online instructor-led training
• Price: ₹56,000
• Eligibility: Open to all learners
• Certificate: Microsoft certificate upon completion

10. Intro to Machine Learning with TensorFlow — Udacity

This introductory programme covers foundational machine learning algorithms, starting from data cleaning and supervised learning models and progressing to deep learning and unsupervised techniques. Practical exercises are integrated at each stage, giving learners hands-on experience with TensorFlow alongside conceptual understanding.

• Duration: Approximately 3 months
• Eligibility: Basic Python and statistics knowledge recommended

Choosing the Right Course for Your Role

The courses above span a wide range of investment levels — from free audits to multi-month master’s programmes. Before enrolling, consider the following:

• DevOps / Cloud engineers will get the most immediate value from the Cloud and DevOps Architect Master’s Course or the Python Networking and Security course.
• Data engineers and analysts should look at the Big Data Analytics certification or the Data Science Architect programme.
• Machine learning practitioners can start with the Coursera ML course or the TensorFlow intro and graduate to the Deep Learning Explained or AI Master’s course.
• Full-stack developers moving into data-driven products will benefit from the Django and AngularJS course alongside a BI or SQL foundation.

Prices and course structures change regularly. Always verify the current syllabus, pricing, and certification status directly on the provider’s website before enrolling.

Apple’s Growing Dependence on Cloud Infrastructure

Apple’s business has evolved well beyond hardware. Services such as iCloud, Apple Music, Apple News Plus, Siri, and Maps now run continuously across a reported 1.5 billion active Apple devices worldwide. Delivering those services at scale demands serious cloud infrastructure — infrastructure that Apple does not yet own entirely.

According to CNBC reporting, Apple pays approximately $30 million per month to Amazon Web Services (AWS), making it one of AWS’s largest enterprise customers. Apple has never officially confirmed which specific services run on AWS beyond iCloud. The company also runs some workloads on Google Cloud Platform.

This reliance on third-party cloud providers is notable given Apple’s otherwise tight control over its hardware, software, and silicon. It raises an obvious strategic question: could Apple build or acquire the cloud capacity to reduce — or eliminate — that dependency?

Apple Cloud Infrastructure (ACI): Apple’s Internal Cloud Team

Apple is not a passive cloud consumer. The company operates its own Apple Cloud Infrastructure (ACI) team, which designs and manages the systems that underpin Apple’s first-party services, including iCloud, iTunes, Siri, and Maps. ACI handles data-center operations, distributed systems engineering, and the networking fabric that connects Apple’s global service infrastructure.

The existence of ACI signals that Apple understands the strategic value of cloud ownership. However, running internal infrastructure for your own services is a fundamentally different undertaking from offering those services commercially to external customers — which is what AWS, Azure, and Google Cloud do.

The Public Cloud Landscape Apple Would Be Entering

If Apple were to consider expanding into the public cloud market, it would face deeply entrenched incumbents. As of early 2020, the major providers reported the following (figures sourced from public earnings statements available at the time of original reporting):

• AWS (Amazon Web Services): Crossed $10 billion in quarterly revenue for the first time in Q1 2020, reporting $10.2 billion — a 33% year-over-year increase. AWS represented roughly 13.5% of Amazon’s total revenue that quarter.
• Google Cloud: Generated $2.78 billion in Q1 2020, a 52% increase year-over-year. That pace put Google Cloud on track for approximately $10 billion in annualized revenue for 2020.
• Microsoft Azure: Microsoft does not break out Azure revenue separately, but reported 59% year-over-year growth for Azure in Q1 2020.

AWS retains a clear first-mover advantage — it launched commercially in 2006, several years before Azure (2010) and Google Cloud Platform gained meaningful enterprise traction. Closing that gap has cost Microsoft and Google billions of dollars in capital expenditure and years of enterprise sales effort.

How AWS Was Built: A Lesson in Accidental Platform Thinking

Understanding AWS’s origin story is useful context for evaluating whether any company — Apple included — could realistically challenge it.

Amazon began in 1994 as an online bookstore. By 2000, rapid growth had exposed serious scalability problems in its internal systems. That year, Amazon attempted to build Merchant.com, a platform letting third-party sellers build storefronts on top of Amazon’s infrastructure. The project quickly revealed a deeper problem: Amazon’s internal services were tightly coupled and poorly documented, making it impossible to expose them cleanly to external developers.

Amazon’s response was to force internal teams to communicate exclusively through well-defined APIs — a mandate often attributed to Jeff Bezos. This discipline transformed Amazon’s engineering culture and produced a set of reusable, composable services for compute, storage, and databases.

Andy Jassy, then Amazon’s chief of staff and later AWS CEO, identified that developers were repeatedly rebuilding the same foundational infrastructure for every new application. His insight was to productize those foundations — compute, storage, queuing, databases — so they could be provisioned on demand rather than rebuilt from scratch.

By 2003, the team had recognized that it had effectively built the operating system for internet applications. AWS launched publicly in 2006 as Amazon Elastic Compute Cloud (EC2), giving external developers access to scalable virtual compute capacity for the first time at commodity pricing.

Could Apple Realistically Compete in Public Cloud?

Entering the public cloud market is not simply a matter of building more data centers. It requires:

• A developer ecosystem: AWS, Azure, and GCP have millions of developers, hundreds of managed services, and deep toolchain integrations. Apple’s developer ecosystem is strong, but it is oriented toward consumer app development, not enterprise infrastructure.
• Enterprise sales motion: Cloud deals at scale require dedicated enterprise sales and support organizations. Apple has limited experience selling to enterprise IT buyers at this level.
• Commoditized pricing: Public cloud competes aggressively on price. Apple’s brand is built on premium products, not commodity compute.
• Regulatory appetite: Hosting enterprise and government workloads brings compliance requirements (FedRAMP, HIPAA, SOC 2, GDPR) that AWS and Azure have spent years satisfying.

A more plausible near-term scenario is that Apple continues to expand ACI to reduce its AWS and Google Cloud spend — essentially internalizing more of its own infrastructure cost — without ever offering those capabilities to external customers. This would be consistent with Apple’s historical preference for vertical integration as a means of controlling user experience and margin, rather than as a revenue-generating platform for third parties.

Key Takeaways

• Apple spends an estimated $30 million per month on AWS and also uses Google Cloud for some services.
• Apple’s internal Apple Cloud Infrastructure (ACI) team manages systems for iCloud, Siri, Maps, and other first-party services.
• AWS, Azure, and Google Cloud collectively dominate public cloud with multi-year head starts and deeply entrenched enterprise relationships.
• Apple has the capital and engineering talent to reduce cloud dependency, but entering the commercial public cloud market would require a fundamentally different business model and go-to-market strategy.
• No public announcement from Apple as of mid-2020 indicated plans to offer cloud services commercially to external customers.

Why AWS Certifications Still Matter for IT Careers

AWS consistently ranks as the largest cloud provider by market share. Organizations across finance, healthcare, media, retail, and the public sector rely on AWS infrastructure, which means hiring managers across those verticals actively look for AWS expertise when filling data engineering, DevOps, and solutions architect roles. An AWS specialty certification serves as an objective proof of that expertise during a competitive hiring process.

While general cloud literacy is increasingly common, specialty certifications target practitioners who can design, build, and secure complex workloads on a specific domain—such as big data and analytics—rather than simply describing what a service does.

What the AWS Data Analytics Specialty Exam Covers

AWS retired the original “Big Data – Specialty” exam and replaced it with the AWS Certified Data Analytics – Specialty (DAS-C01). The updated exam tests five primary domains:

• Collection – Kinesis Data Streams, Kinesis Data Firehose, AWS Glue, Database Migration Service (DMS), and choosing the right ingestion pattern for throughput and latency requirements.
• Storage and Data Management – S3 storage classes, data lifecycle policies, DynamoDB, and Lake Formation for organizing data lakes.
• Processing – Amazon EMR (Elastic MapReduce), AWS Glue ETL jobs, Lambda for event-driven transformation, and Spark/Hive on EMR.
• Analysis and Visualization – Amazon Athena for serverless SQL querying, Amazon Redshift for data warehousing, QuickSight for BI dashboards, and OpenSearch (formerly Elasticsearch Service).
• Security – Encryption at rest and in transit, IAM policies, VPC configurations, Lake Formation permissions, and CloudTrail auditing across the data pipeline.

Key AWS Services to Master Before the Exam

Amazon EMR (Elastic MapReduce)

EMR is AWS’s managed cluster platform for running big data frameworks such as Apache Spark, Hadoop, Hive, and Presto. Exam questions often focus on cluster sizing, instance fleet versus instance group configurations, cost optimization with Spot Instances, and choosing between persistent and transient clusters depending on the workload.

AWS Glue

Glue is a serverless ETL service that also provides a central data catalog. Understanding the crawler-to-catalog-to-job pipeline, DynamicFrame vs. DataFrame in Glue ETL scripts, job bookmarks for incremental processing, and Glue DataBrew for no-code transformations is essential for the exam and for real-world data engineering work.

AWS Lambda

Lambda appears in data pipelines as the event-driven glue between services—triggering transformations when a file lands in S3, reacting to Kinesis stream records, or orchestrating lightweight processing steps. Know its concurrency limits, execution timeout constraints, and integration with Step Functions for complex workflows.

Amazon Kinesis

Real-time data ingestion is a core exam topic. Understand the difference between Kinesis Data Streams (configurable retention, custom consumers, replay capability) and Kinesis Data Firehose (managed delivery to S3, Redshift, or OpenSearch with built-in transformation via Lambda).

Amazon Redshift

Redshift is AWS’s columnar data warehouse, and it carries significant weight in the exam. Focus on distribution styles (KEY, ALL, EVEN, AUTO), sort keys, Redshift Spectrum for querying S3 without loading data, COPY command tuning, and Workload Management (WLM) configuration.

Prerequisites and Recommended Experience

AWS recommends at least five years of experience with data analytics and two or more years of hands-on AWS experience before attempting the Data Analytics – Specialty exam. Candidates who have already passed the AWS Certified Cloud Practitioner or AWS Certified Solutions Architect – Associate exams will find the foundational concepts familiar and can focus their study time on the analytics-specific services.

Practical lab work matters more than video hours alone. Set up an end-to-end pipeline—ingest records with Kinesis, process them with Glue or Lambda, store the output in S3 and Redshift, then query with Athena—before sitting the exam.

How to Structure Your Study Plan

1. Review the official exam guide. AWS publishes a detailed exam guide listing every domain and the percentage of questions drawn from each. Use it to weight your study time accordingly.
2. Complete a structured video course. Multi-hour prep courses (typically 15–20 hours of content) provide systematic coverage of the exam domains. Look for courses updated to the DAS-C01 blueprint.
3. Work through hands-on labs. AWS offers a free tier with access to many of the relevant services. AWS Skill Builder also provides guided labs and practice environments.
4. Practice with timed exams. Official AWS practice exams and third-party question banks help you identify weak areas and build test-taking stamina. The exam is 65 questions in 180 minutes.
5. Review AWS whitepapers and FAQs. The AWS Big Data Whitepaper, the Streaming Data Solutions on AWS whitepaper, and the FAQs for Redshift, EMR, and Kinesis are common sources for nuanced exam questions.

Career Outlook for AWS-Certified Data Specialists

AWS-certified professionals are in demand across a wide range of industries. Roles that commonly list AWS Data Analytics or Big Data specialty credentials as a requirement or preference include:

• Data Engineer
• Cloud Data Architect
• Analytics Solutions Architect
• BI Platform Engineer
• DataOps / MLOps Engineer

Compensation for certified AWS data engineers varies significantly by geography, industry, and seniority. Researching current salary ranges on sites such as Levels.fyi, LinkedIn Salary, or the Stack Overflow Developer Survey will give you the most accurate and up-to-date figures for your specific market.

Bottom Line

The AWS Certified Data Analytics – Specialty exam is a rigorous credential that rewards genuine platform experience. It is not a shortcut to a high-paying role—but for engineers who already work with data pipelines and want to formalize and validate their AWS expertise, it is a well-recognized signal that can open doors to more senior positions. Pair structured video instruction with consistent hands-on lab practice, and use the official exam guide to keep your preparation focused.

Note: AWS periodically updates its certification catalog and exam blueprints. Always check the AWS Certification page for the current exam version and guide before purchasing study materials.

What Is the Global Big Data Market?

Big data is more than a description of large datasets. It refers to the full ecosystem of technologies used to capture, store, manage, and analyze large, fast-moving, and highly variable collections of data in order to solve complex business problems. Organizations adopt big data platforms to reduce the cost and complexity of managing data at scale, unlock analytical insights that traditional databases cannot deliver, and support real-time decision-making across the enterprise.

The term originally emerged to describe datasets too large for conventional database tools to handle. Today, it encompasses distributed storage systems, stream processing engines, data lakes, machine learning pipelines, and managed cloud analytics services.

Key Players in the Global Big Data Market

The global big data market is highly competitive, with established enterprise vendors, cloud hyperscalers, and specialist analytics companies all vying for market share. Major organizations active in this space include:

• IBM Corporation – enterprise data warehousing, AI-driven analytics (Watson), and hybrid cloud data platforms
• Oracle Corporation – cloud databases, Autonomous Data Warehouse, and integrated analytics
• Microsoft Corporation – Azure Synapse Analytics, HDInsight, and Power BI
• Amazon Web Services (AWS) – S3, EMR, Redshift, Kinesis, and a broad managed analytics portfolio
• Google – BigQuery, Dataflow, Dataproc, and Vertex AI
• SAP SE – HANA in-memory platform and enterprise analytics
• Hewlett Packard Enterprise (HPE) – Ezmeral Data Fabric and high-performance compute for analytics workloads
• Teradata Corporation – cloud-native data warehouse and Vantage analytics platform
• Splunk – machine data analytics, SIEM, and operational intelligence
• Cloudera – unified data platform combining legacy Hortonworks and Cloudera distributions on hybrid cloud
• Hortonworks – now merged with Cloudera; historically a major open-source Hadoop distribution vendor
• Palantir Technologies – data integration and analytics for government, defense, and enterprise
• Hitachi – industrial IoT data analytics and Lumada platform
• SAS Institute – statistical analytics, data management, and AI/ML tooling
• Dell Technologies – data storage infrastructure and converged analytics appliances
• Accenture – systems integration and managed analytics services
• Fujitsu – HPC-based analytics and enterprise IT services
• Datameer – data transformation and exploration on cloud data warehouses
• Actian – high-performance data integration and analytics database
• Guavus – telecom-focused big data analytics (acquired by Thales)

Data Types Covered by Big Data Solutions

Big data platforms are generally evaluated against three core data categories:

• Structured Data – Rows and columns stored in relational databases or data warehouses; amenable to SQL-based querying.
• Semi-Structured Data – Formats such as JSON, XML, and log files that carry some organizational tags but do not conform to a rigid schema.
• Unstructured Data – Free-form content including text documents, images, video, audio, and social media streams; typically requires specialized processing pipelines and AI/ML models to extract value.

Modern data lakehouse architectures aim to handle all three categories within a single platform, reducing the need for separate storage and processing silos.

Industry Verticals Driving Big Data Adoption

Demand for big data capabilities spans virtually every industry sector. The following verticals represent the primary application segments:

• Banking, Financial Services, and Insurance (BFSI) – fraud detection, risk modeling, algorithmic trading, and customer analytics
• Healthcare and Life Sciences – genomics, clinical trial analysis, patient data management, and predictive diagnostics
• Retail and Consumer Goods – personalization engines, supply chain optimization, and demand forecasting
• IT and Telecommunications – network performance monitoring, churn prediction, and service assurance
• Government and Defense – intelligence analysis, fraud detection in public programs, and smart city initiatives
• Manufacturing – predictive maintenance, quality control, and IoT sensor analytics
• Media and Entertainment – content recommendation, audience segmentation, and ad targeting
• Transportation and Logistics – route optimization, fleet management, and real-time tracking
• Energy and Utilities – smart grid analytics, predictive asset maintenance, and consumption forecasting
• Academia and Research – large-scale scientific datasets, climate modeling, and bioinformatics

Regional Market Landscape

Big data investment is global, though adoption rates and dominant use cases vary by region:

• North America (United States, Canada, Mexico) – the most mature market, driven by early cloud adoption, large technology vendors, and heavy investment in financial services and healthcare analytics
• Europe (UK, Germany, France, Italy, Spain, Russia) – growing adoption shaped by GDPR compliance requirements, which have heightened demand for data governance and privacy-preserving analytics
• Asia-Pacific (China, India, Japan, South Korea, Singapore, Indonesia, Australia) – the fastest-growing region, fueled by rapid digitalization, e-commerce expansion, and government-led smart city and manufacturing initiatives
• South and Central America (Brazil, Argentina, Chile) – emerging market with growth driven by fintech, agriculture analytics, and expanding cloud infrastructure
• Middle East and Africa (Saudi Arabia, UAE, South Africa, Turkey, Egypt) – investment focused on oil and gas analytics, smart government programs, and financial services modernization

What to Look for When Evaluating Big Data Market Research

DevOps and platform engineering teams often need to contextualize vendor decisions against broader market trends. When reviewing any big data market report, consider the following criteria:

• Forecast methodology – How are market size figures derived? Top-down (analyst estimates) and bottom-up (revenue aggregation) methods produce materially different numbers.
• Coverage scope – Does the report include open-source platforms (Apache Spark, Flink, Kafka) alongside commercial vendors? Open-source adoption can be underrepresented in revenue-based market sizing.
• Segmentation granularity – Country-level or sub-vertical breakdowns are typically locked behind premium report tiers.
• Recency – The big data vendor landscape shifts quickly. Cloudera and Hortonworks merged in 2019; Guavus was acquired. Reports older than 12–18 months may reflect an outdated competitive picture.
• Data type treatment – Ensure the report distinguishes between big data infrastructure (storage, compute) and analytics software layers, as growth rates differ substantially.

Conclusion

The global big data market encompasses a wide range of vendors, data types, and industry use cases. Established enterprise software companies, cloud hyperscalers, and specialist analytics vendors continue to compete and consolidate, driven by enterprise demand for scalable, cost-effective data platforms. For DevOps and data engineering practitioners, understanding the competitive landscape helps inform technology selection, vendor negotiation, and long-term platform strategy. Validating any specific revenue figures or growth forecasts against primary sources or multiple independent research firms before using them in business cases is strongly recommended.

What Are Microservices and Why Do They Matter in Healthcare?

Microservices architecture is a style of software design derived from service-oriented architecture (SOA). Instead of building one large monolithic application, developers decompose functionality into small, independently deployable services. Each service owns a specific business capability — for example, patient scheduling, claims processing, or lab result retrieval — and communicates with other services through well-defined APIs.

In healthcare IT, this pattern addresses several long-standing problems: tightly coupled legacy systems, slow release cycles, and the difficulty of integrating data across providers, payers, and regulators. Because individual microservices can be deployed, scaled, or replaced without touching the rest of the application, engineering teams can ship fixes and new features far more quickly than with traditional monoliths.

Core Benefits for Healthcare Engineering Teams

• Portability and interoperability: Services built around standard APIs (REST, gRPC, HL7 FHIR) can be consumed by different front-ends — web portals, mobile apps, partner systems — without duplicating business logic.
• Accelerated time to market: Independent deployment pipelines mean a change to the billing service does not require a full regression cycle on clinical modules.
• Independent replaceability: A legacy claims-adjudication component can be rewritten and swapped out without a “big-bang” migration of the entire platform.
• Targeted scalability: High-throughput workloads such as real-time eligibility checks can be scaled horizontally without over-provisioning the entire application stack.
• Fault isolation: A failure in one service does not cascade to bring down unrelated services, improving overall system resilience.

Market Context (c. 2019–2026)

According to the Data Bridge Market Research report that prompted this article, the global microservices in healthcare market was valued at approximately USD 132 million in 2018 and was projected to reach roughly USD 644 million by 2026, representing a compound annual growth rate (CAGR) of around 21.9%. The report attributed growth primarily to demand for faster software delivery cycles and the need to integrate disparate healthcare data sources.

Reported demand-side growth drivers included rising government initiatives around health data interoperability, growing healthcare infrastructure investment, and increasing awareness of cloud-native architecture patterns among health system IT leadership.

Market Segmentation

The report segmented the market across three primary dimensions: component type, deployment model, and end-user category.

By Component

• Platforms — middleware and orchestration tooling (e.g., service meshes, API gateways, container orchestrators)
• Services
  • Consulting Services
  • Integration Services
  • Training, Support, and Maintenance Services

By Deployment Model

• Cloud-Based
  • Public Cloud
  • Private Cloud
  • Hybrid Cloud
• On-Premises

Hybrid cloud deployments are particularly relevant in healthcare, where regulatory requirements (HIPAA in the US, GDPR in Europe) may mandate that certain patient data remain on-premises or within a controlled private environment, while less sensitive workloads run on public cloud infrastructure.

By End-User

• Healthcare Providers (hospitals, clinics, physician groups)
• Healthcare Payers (insurers, managed care organizations)
• Life Sciences Organizations (pharmaceutical and medical device companies)
• Clinical Laboratories

Key Technology Vendors

The report identified the following companies as significant participants in the microservices tooling and services space relevant to healthcare:

• Amazon Web Services (AWS) — cloud infrastructure, container services (ECS, EKS), and AWS App Mesh
• Microsoft Azure — Azure Service Fabric, AKS, API Management
• IBM — hybrid cloud integration, IBM Cloud Pak for Integration
• Oracle — Oracle Cloud Infrastructure, API Gateway
• Salesforce.com — Health Cloud, platform APIs
• Pivotal Software (now VMware Tanzu) — Spring Boot/Spring Cloud microservices frameworks, Cloud Foundry
• Infosys — digital transformation and microservices consulting for healthcare clients
• Cognizant — healthcare IT services and cloud-native application modernization
• Syntel (now Atos Syntel) — application modernization services
• MuleSoft (a Salesforce company) — API-led connectivity, Anypoint Platform
• NGINX / F5 — reverse proxy, API gateway, and service mesh components
• Software AG — integration platform and API management
• CA Technologies (now Broadcom) — API management and DevOps tooling
• Datawire — Ambassador API gateway (open-source, Kubernetes-native)

Notable Vendor Developments (2018)

The original report highlighted two specific developments from late 2018:

• AWS App Mesh (November 2018): Amazon Web Services launched AWS App Mesh, a service mesh that provides application-level networking to make it easy for services to communicate with each other across multiple types of compute infrastructure. It captures metrics, logs, and traces from every microservice, helping operators monitor and control traffic between services without modifying application code.
• Infosys–Hitachi joint venture (December 2018): Infosys announced a joint venture with Hitachi, Panasonic, and Pasona to expand its presence in Japan. The arrangement was framed as accelerating business process transformation for clients in the region, though the direct healthcare-specific implications were not detailed in the source material.

Architectural Considerations for Healthcare Microservices

Implementing microservices in a healthcare context introduces challenges beyond those found in typical enterprise software projects. Engineering teams should plan for the following:

Interoperability Standards

Healthcare data exchange standards such as HL7 FHIR (Fast Healthcare Interoperability Resources) and HL7 v2 are central to any microservices design that must share clinical data across organizations. Building FHIR-compliant APIs into service contracts from the start reduces integration friction with EHR systems, payer platforms, and government registries.

Security and Compliance

Every service boundary is a potential attack surface. mTLS (mutual TLS) between services, enforced through a service mesh such as Istio or AWS App Mesh, ensures that only authorized services can communicate. OAuth 2.0 and SMART on FHIR provide authorization frameworks for patient-facing applications. Audit logging at the API gateway and at the service level is typically required for HIPAA compliance.

Data Consistency

Each microservice ideally owns its own data store (the “database per service” pattern). In healthcare, this must be balanced against the need for consistent, accurate patient records. Saga patterns and event-driven architectures using message brokers (Apache Kafka, Amazon SNS/SQS) are commonly used to maintain eventual consistency across service boundaries without introducing tight coupling.

Observability

Distributed tracing (OpenTelemetry, AWS X-Ray, Jaeger), centralized logging, and metrics aggregation are non-negotiable in production healthcare environments where a degraded service can directly affect patient care workflows. Establishing SLOs and alerting before going live is strongly recommended.

Summary

Microservices architecture offers healthcare IT organizations a practical path to modernizing legacy systems, accelerating feature delivery, and achieving the interoperability that regulators and patients increasingly demand. The technology vendor landscape is well-established, with both hyperscale cloud providers and specialist integration platforms offering mature tooling. The key challenges — security, compliance, data consistency, and observability — are solvable with established patterns, but require deliberate planning from the start of any migration effort.

For teams evaluating a microservices migration, start with a bounded, non-critical service to validate your deployment pipeline, observability stack, and security controls before decomposing core clinical or financial systems.

What Is Batch Processing?

Batch processing refers to executing a series of compute jobs that run to completion without requiring ongoing human interaction. Unlike interactive or real-time workloads, batch jobs are queued and processed sequentially or in parallel based on available resources and scheduling rules.

Classic examples include:

• Nightly ETL (Extract, Transform, Load) pipelines that process millions of database records
• Genomic sequencing pipelines that compare DNA variants across large sample sets
• Finite element analysis (FEA) simulations for materials safety testing
• Financial risk calculations run across thousands of portfolio scenarios
• Rendering pipelines for animation or scientific visualization

In on-premises environments, these jobs are constrained by available compute resources. A job must wait until enough CPU, memory, and storage are free — which can create significant bottlenecks on project timelines. Cloud computing eliminates this fixed-capacity ceiling.

What Is AWS Batch?

AWS Batch is a fully managed batch computing service that dynamically provisions the optimal quantity and type of compute resources — such as CPU or memory-optimized Amazon EC2 instances — based on the volume and specific requirements of the batch jobs submitted. Engineers define the jobs; AWS Batch handles the scheduling, resource provisioning, and execution.

There is no batch processing software to install or cluster to configure. AWS Batch integrates natively with other AWS services including Amazon EC2, EC2 Spot Instances, Amazon ECS, Amazon S3, and AWS IAM, orchestrating all the necessary compute, storage, and networking resources automatically.

Core Concepts in AWS Batch

Understanding AWS Batch requires familiarity with four primary constructs:

1. Jobs

A Job is the unit of work submitted to AWS Batch. It can be a shell script, a Docker container image, or an executable. Jobs are submitted with a job definition that specifies the container image, vCPU and memory requirements, IAM role, and any environment variables or command overrides.

2. Job Queues

Jobs are submitted to Job Queues, which hold jobs until compute resources are available. You can configure multiple queues with different priorities, and AWS Batch will schedule higher-priority jobs first. A single job queue can be associated with multiple compute environments.

3. Compute Environments

A Compute Environment defines the EC2 instance types, minimum and maximum vCPUs, VPC and subnet configuration, and whether to use On-Demand or Spot Instances. AWS Batch manages the EC2 fleet within this environment, scaling it up or down in response to job queue depth.

4. Job Definitions

A Job Definition acts as a template specifying how jobs should run: which Docker image to use, how much CPU and memory to allocate, what IAM permissions are needed, retry strategies, and timeout values. Job definitions are versioned, making it straightforward to roll back to a previous configuration.

How AWS Batch Schedules and Runs Jobs

When a job is submitted to a queue, AWS Batch evaluates available compute capacity in the associated compute environments. If capacity is insufficient, it automatically launches new EC2 instances (or Fargate tasks, if using the Fargate compute environment) to meet demand. Once the job completes, AWS Batch scales down the fleet to avoid unnecessary costs.

Jobs can be configured with dependencies, so that downstream jobs only begin once upstream jobs have completed successfully. This makes it straightforward to build multi-stage pipelines — for example, a data preprocessing job that must finish before an analytics job begins.

Cost Optimization with EC2 Spot Instances

One of the most significant financial benefits of AWS Batch is its native integration with Amazon EC2 Spot Instances. Spot Instances are spare EC2 capacity offered at a discount compared to On-Demand pricing. Because batch jobs are generally interruptible and can be retried, they are well suited to Spot Instance usage.

AWS Batch can be configured to use a mix of On-Demand and Spot Instance capacity. When Spot capacity is interrupted by AWS, AWS Batch can automatically retry affected jobs, minimizing the operational impact. For many batch workloads, this approach can substantially reduce compute costs compared to running equivalent on-premises infrastructure or On-Demand EC2 instances exclusively.

Key Benefits of AWS Batch for DevOps Teams

• No infrastructure to manage: AWS Batch provisions, scales, and terminates compute resources automatically. Engineers do not need to configure or maintain EC2 clusters, job schedulers, or queue managers.
• Pay only for what you use: Because compute resources are spun up on demand and shut down when jobs complete, you are billed only for the EC2 or Fargate resources consumed during job execution.
• Scales to any workload size: Whether you have ten jobs or ten thousand, AWS Batch scales the underlying compute environment to match demand without manual intervention.
• Docker-native: Jobs run as Docker containers, making it straightforward to package dependencies, ensure reproducibility, and version control job definitions alongside application code.
• Integrated with the AWS ecosystem: AWS Batch works natively with Amazon S3 for data input and output, AWS CloudWatch for logs and metrics, AWS Step Functions for workflow orchestration, and IAM for fine-grained access control.
• Array jobs and dependencies: Array jobs allow you to run many independent jobs (such as processing one file per job) with a single submission. Job dependencies let you chain stages in a pipeline without building a separate orchestration layer.

AWS Batch vs. Running Your Own Job Scheduler

Teams that manage batch workloads on-premises typically rely on schedulers such as Slurm, PBS Pro, LSF, or Grid Engine, combined with a fixed pool of servers. This approach requires dedicated infrastructure investment, ongoing software maintenance, capacity planning, and expert knowledge of the scheduler itself. When demand spikes, jobs queue behind fixed capacity constraints.

AWS Batch replaces this stack with a managed service. The tradeoff is that workloads must be containerized (or use supported runtime models), and teams give up some lower-level control over scheduler configuration. For the majority of batch workloads — particularly those with variable demand — the operational simplicity and elastic capacity of AWS Batch outweigh these constraints.

Common Use Cases

• Bioinformatics and genomics: Processing sequencing data at scale, running variant calling pipelines, or performing protein structure analysis across large datasets.
• Financial services: Running end-of-day risk calculations, stress testing, and Monte Carlo simulations across large portfolios.
• Media and entertainment: Transcoding video files, rendering animation frames, or generating image thumbnails at scale.
• Machine learning preprocessing: Transforming, normalizing, and splitting large training datasets before feeding them into model training pipelines.
• Log and data analytics: Processing and aggregating large volumes of log data on a scheduled basis.

Getting Started

A basic AWS Batch setup involves the following steps:

1. Containerize your job: Package your workload as a Docker image and push it to Amazon ECR (Elastic Container Registry) or another accessible container registry.
2. Create a Compute Environment: Define the EC2 instance types, minimum and maximum vCPU limits, VPC settings, and whether to use On-Demand or Spot capacity.
3. Create a Job Queue: Associate the queue with one or more compute environments and assign priorities.
4. Create a Job Definition: Specify the container image, resource requirements (vCPUs and memory), IAM role, and any command or environment variable overrides.
5. Submit a Job: Use the AWS Management Console, AWS CLI, or an SDK to submit a job to the queue. AWS Batch handles the rest.

Video Overview

Summary

AWS Batch removes the operational overhead of managing batch compute infrastructure by automatically provisioning, scaling, and releasing compute resources in response to your job queue. It integrates with the broader AWS ecosystem, supports containerized workloads, and enables significant cost savings through Spot Instance usage. For DevOps teams supporting data-intensive workloads — whether in genomics, finance, media, or analytics — AWS Batch provides a scalable, cost-effective, and largely self-managing foundation for batch computing.

This article unpacks each ingredient, explains why it matters, and shows how the three work together to deliver the speed, agility, and efficiency that modern enterprises need.

1. DevOps: Breaking Down Silos with Collaboration, Automation, and Continuous Delivery

DevOps is a cultural and technical movement that dismantles the traditional wall between software development and IT operations teams. Where those teams once worked in isolation—development writing code and throwing it over the fence to operations to deploy—DevOps integrates both disciplines into a shared, continuous workflow.

What DevOps Changes in Practice

• Faster release cycles: Continuous Integration and Continuous Delivery (CI/CD) pipelines automate testing and deployment, reducing the time from code commit to production release from weeks to hours or minutes.
• Shared ownership: Developers and operations engineers share responsibility for reliability, security, and performance—not just functionality.
• Automated feedback loops: Monitoring and alerting tools provide real-time signals that flow back into the development process, enabling rapid iteration.
• Reduced mean time to recovery (MTTR): Because deployments are smaller and more frequent, failures are easier to isolate and roll back.

Large-scale consumer platforms have demonstrated that DevOps practices enable an organization to develop, integrate, test, and upgrade applications continuously at a pace that would be impossible using traditional waterfall or siloed delivery models. The same principles apply to enterprise software, internal tooling, and customer-facing products alike.

For enterprises looking to stay responsive to customer needs, adopting DevOps means aligning the rhythm of software delivery with business objectives rather than with fixed release schedules imposed by process bureaucracy.

2. Containers: Portable, Isolated, and Independently Deployable Units

Containers represent the next evolution beyond virtual machines (VMs). Where a VM emulates an entire operating system—carrying significant overhead—a container packages only the application code and its specific dependencies into a lightweight, self-contained unit that shares the host OS kernel.

Why Containers Matter for IT Transformation

• Portability: A container image runs consistently across a developer’s laptop, a CI/CD pipeline, an on-premises server, or a public cloud—eliminating the classic “works on my machine” problem.
• Microservices alignment: Containers allow applications to be decomposed into loosely coupled services that can be developed, deployed, scaled, and updated independently.
• Resource efficiency: Containers start in seconds and consume far less memory and CPU overhead than equivalent VMs, enabling higher density on the same hardware.
• Faster iteration: Teams can push changes to individual services without redeploying the entire application stack.

Managing Container Sprawl with Kubernetes

As adoption scales, organizations inevitably encounter container sprawl—the same challenge that arose with server sprawl and VM sprawl in earlier infrastructure eras. Managing hundreds or thousands of containers manually is impractical, which is where container orchestration becomes essential.

Kubernetes has emerged as the de facto standard for container orchestration. It provides:

• Automated scheduling: Kubernetes places containers across a cluster of nodes based on available resources and defined constraints.
• Self-healing: Failed containers are automatically restarted; unhealthy nodes are drained and workloads rescheduled.
• Horizontal scaling: Workloads scale up or down automatically based on CPU usage, memory consumption, or custom metrics.
• Service discovery and load balancing: Kubernetes manages internal DNS and load balancing so services can find and communicate with each other reliably.
• Declarative configuration: Infrastructure and application state is defined in version-controlled YAML manifests, enabling GitOps workflows.

Kubernetes can run on-premises, in managed cloud offerings (such as Amazon EKS, Google GKE, or Azure AKS), or in hybrid configurations—giving enterprises flexibility in how and where they deploy containerized workloads.

3. Software-Defined Infrastructure (SDI) and Software-Defined Data Centres (SDDC)

Traditional data centres are built on proprietary hardware with fixed capacity and manual configuration processes that are too slow for the pace of modern application delivery. Software-Defined Infrastructure decouples the control plane from physical hardware, allowing compute, networking, and storage resources to be provisioned, configured, and managed programmatically through software.

Core Characteristics of SDI/SDDC

• Pooled resources: Compute, storage, and network capacity are abstracted into dynamic resource pools that can be allocated on demand rather than reserved in advance.
• Policy-driven automation: Provisioning, scaling, and decommissioning of infrastructure resources are driven by software policies, reducing manual intervention and human error.
• Commodity hardware: SDI software runs on standard x86 hardware, reducing dependence on expensive proprietary appliances and lowering capital expenditure.
• Agility: Infrastructure changes that once required physical work—racking servers, re-cabling networks—can be executed in minutes through a management API or UI.
• Consistency: Infrastructure-as-code practices apply equally to SDI, meaning data centre configurations can be version-controlled, peer-reviewed, and tested in the same way as application code.

The shift from hardware-defined to software-defined data centres also frees IT teams from routine administrative tasks, allowing them to focus engineering effort on building and improving services rather than maintaining physical infrastructure.

Why These Three Ingredients Are Interlocked

DevOps, containers, and SDI are not independent choices—they reinforce and depend on each other:

• DevOps needs containers to make CI/CD pipelines reliable and environment-agnostic. Containers guarantee that the artifact tested in the pipeline is identical to what runs in production.
• Containers need orchestration and infrastructure to run at scale. Kubernetes abstracts the underlying infrastructure, and SDI ensures that infrastructure is elastic enough to accommodate the dynamic resource demands of containerized workloads.
• SDI benefits from DevOps culture because infrastructure-as-code, automated testing of configuration changes, and continuous delivery practices apply directly to managing software-defined resources.

Together, these three pillars form a self-reinforcing platform: DevOps practices drive faster delivery, containers provide the portable deployment unit, and software-defined infrastructure provides the programmable, elastic foundation that makes the whole system scalable and resilient.

Putting It Together: A Practical Starting Point

For engineering teams beginning or advancing an IT transformation journey, a pragmatic sequence often looks like this:

1. Establish DevOps foundations first. Invest in CI/CD tooling, shared monitoring, and cross-functional team structures before migrating workloads. Culture change is slower than technology change.
2. Containerize new workloads before legacy ones. Start with greenfield services or stateless applications where containerization is straightforward. Build organizational competency before tackling stateful or complex legacy systems.
3. Adopt Kubernetes incrementally. Managed Kubernetes services on a cloud provider reduce operational burden during the learning curve. Move to self-managed or on-premises clusters once the team has sufficient operational maturity.
4. Introduce SDI alongside your container strategy. Whether through a hyper-converged infrastructure platform, a cloud provider’s virtual networking and storage services, or an open-source SDN solution, ensure your infrastructure layer can be programmatically controlled to meet the dynamic demands of container orchestration.
5. Measure and iterate. Track deployment frequency, lead time for changes, change failure rate, and MTTR (the DORA metrics). Use data to guide further investment.

Conclusion

IT transformation is not a single project with a finish line—it is an ongoing capability-building process. DevOps, containers, and Software-Defined Infrastructure are the three technical pillars that, when implemented together and supported by the right organizational culture, give enterprises the agility, speed, and efficiency required to compete in a digital-first market. The technologies are mature and widely proven. The differentiator is how effectively an organization combines and operationalizes all three.

Microsoft Awarded the Pentagon’s Landmark JEDI Cloud Contract

In a result that surprised much of the cloud industry, Microsoft — not AWS — was awarded the U.S. Department of Defense’s Joint Enterprise Defense Infrastructure (JEDI) contract in late October 2019. The contract carries a ceiling value of up to $10 billion spread across a 10-year period, making it one of the largest cloud infrastructure deals ever awarded by a government entity.

What Is the JEDI Contract?

The JEDI program was first announced in September 2017. According to a November 2017 DoD memo, the contract called for a single cloud provider to build a “highly available, exponentially elastic, secure, resilient cloud computing environment that seamlessly extends from the homefront to the tactical edge.” The Pentagon ultimately plans to migrate approximately 80% of its internal systems to the winning platform, though it maintains a variety of other cloud services alongside JEDI.

The base contract period is two years with $1 million guaranteed. Roughly $210 million was expected to be spent during that initial two-year window, with the remainder of the potential $10 billion subject to rigorous ongoing performance reviews, according to the DoD.

A Competitive and Controversial Bidding Process

The contract attracted bids from IBM, Oracle, Google, AWS, and Microsoft. By April 2019, the DoD had narrowed the field to just AWS and Microsoft. In July 2019, a federal judge dismissed a lawsuit filed by Oracle challenging the procurement process.

AWS had been widely considered the frontrunner. The company’s cloud division had previously won a high-profile $600 million contract with the CIA to build out that agency’s big data and analytics capabilities — a deal that demonstrated AWS’s ability to handle classified government workloads. Microsoft, however, had its own government cloud credentials: in May 2018, the company announced a contract worth hundreds of millions of dollars to provide Azure Government services to a range of U.S. intelligence agencies.

Political Controversy Shadowed the Award

The JEDI procurement became entangled in presidential politics. President Donald Trump — a vocal critic of Amazon CEO Jeff Bezos, who also owns The Washington Post — stated in July 2019 that his administration intended to scrutinize Amazon’s bid following complaints from competing vendors about the award process. Whether that scrutiny influenced the final decision remained unclear at the time of the award.

The DoD defended its process: “All offerors were treated fairly and evaluated consistently with the solicitation’s stated evaluation criteria. Prior to the award, the department conferred with the DOD Inspector General, which informed the decision to proceed.”

AWS Reacts — and Eyes Further Recourse

Amazon expressed clear surprise at the result. An AWS spokesperson said: “AWS is the clear leader in cloud computing, and a detailed assessment purely on the comparative offerings clearly lead to a different conclusion.” At the time of the award, it was not immediately clear whether Amazon would pursue a formal legal challenge — though the company subsequently did file a protest with the U.S. Court of Federal Claims.

Market Context: AWS Still Leads, But Microsoft Closes Ground

At the time of the award, market research firm Synergy Research estimated the global cloud infrastructure market at approximately $100 billion annually. AWS held roughly 33.5% of that market, with Microsoft Azure at approximately 16.5%. While AWS maintained a commanding lead in overall market share, the JEDI win gave Microsoft a powerful, high-visibility reference point for Azure’s readiness to handle the world’s most security-sensitive and mission-critical workloads.

For enterprise and government customers evaluating cloud providers for large, sensitive deployments, a DoD endorsement of this scale carries significant weight — and Microsoft was expected to leverage the JEDI win in future negotiations across both public sector and regulated industry verticals.

What Came Next

Following the initial award, AWS filed a legal challenge that led to a court-ordered temporary restraining order halting Microsoft’s work on the contract. The JEDI contract dispute continued through the courts for years and was ultimately cancelled by the DoD in 2021, which moved on to a new multi-vendor cloud procurement program called the Joint Warfighter Cloud Capability (JWCC). That subsequent program — notably — awarded contracts to multiple vendors including both Microsoft and AWS, as well as Google and Oracle.

Key Takeaways for Cloud and DevOps Practitioners

• Single-vendor government mega-contracts carry inherent risk. The JEDI model — one provider, one contract — proved legally and politically fragile. Multi-vendor approaches, as seen with JWCC, are increasingly preferred for critical government infrastructure.
• Government cloud clearances matter. Both AWS GovCloud and Azure Government had demonstrated classified workload capabilities before JEDI. Organizations in regulated sectors should evaluate providers’ specific compliance certifications, not just general cloud capabilities.
• Market share does not guarantee contract wins. Despite AWS’s dominant position, technical evaluation criteria, pricing, and other factors shaped the outcome.
• Procurement disputes can delay delivery by years. Engineering teams should account for legal and contractual risk when planning long-horizon cloud migrations tied to government or enterprise mega-deals.

Pivotal Makes a Major Bet on Kubernetes

In July 2019, Pivotal announced a significant strategic shift toward Kubernetes, unveiling alpha versions of three Kubernetes-integrated products. The centerpiece was a Kubernetes-powered edition of its flagship Pivotal Application Service (PAS) platform — a move that signaled the company was embedding container orchestration deeply into its core product line.

The announcements came roughly a month after Pivotal — then backed by Dell and VMware — faced sharp criticism from investors over its perceived slowness in adopting Kubernetes, a criticism that coincided with the company scaling back its financial forecasts and a stock price that fell nearly 50 percent. CEO Rob Mee had, however, already flagged the Kubernetes direction during the earnings call, suggesting the product announcements were part of a planned roadmap rather than a reactive pivot.

PAS on Kubernetes: The Key Announcement

The headline product was an alpha release of Pivotal Application Service running on Kubernetes. Pivotal’s own blog described Kubernetes as “the new IaaS” and stated the company was “embedding it into more parts of Pivotal technology.”

The alpha was positioned as a proof-of-concept supporting the most important PAS capabilities, including:

• cf push support for buildpack-based applications, preserving the familiar Cloud Foundry developer experience
• App instances running as Kubernetes pods, integrating PAS workloads natively into the Kubernetes scheduler
• Scaling to at least 50 app instances in this initial release

Future roadmap items at the time included multi-cloud support — specifically AKS on Microsoft Azure and GKE on Google Cloud Platform — as well as the ability to deploy PAS on Kubernetes alongside existing PAS environments, enabling incremental adoption rather than a forced migration.

Pivotal Build Service: From Source Code to OCI Images

The second announcement was an alpha of Pivotal Build Service, integrated with Enterprise PKS (Pivotal and VMware’s joint Kubernetes distribution). Pivotal described the service as a declarative way to build OCI-compatible container images directly from source code, saying it “picks up where the Dockerfile workflow leaves off.”

The practical value proposition was image lifecycle management: the Build Service was designed to keep container images consistent and automatically up to date with the latest base image patches. Pivotal summarized this benefit as eliminating “snowflake containers” — the problem of inconsistent, manually maintained images that diverge from a known good baseline over time.

RabbitMQ for Kubernetes

The third product announced was RabbitMQ for Kubernetes, aimed at simplifying the operational overhead of running the popular open-source message broker inside Kubernetes environments. Pivotal positioned this as reducing the complexity of deploying and managing RabbitMQ clusters as Kubernetes-native workloads.

Alpha Status and Access by Request

All three products were released as alpha software, with general availability timelines not announced at that point. Access was available by request only — a standard approach for early-stage enterprise software intended to manage onboarding, gather structured feedback, and avoid support commitments before the product stabilizes.

These announcements followed the launch of Pivotal Service Mesh in May 2019, which brought Istio and Envoy proxy to the Pivotal platform — also as an alpha program — indicating a broader, multi-quarter effort to build out a cloud-native infrastructure stack around Kubernetes.

Context: Investor Pressure and the Kubernetes Imperative

Pivotal’s share price had fallen close to 50 percent in the weeks before these announcements after the company reported that some deals had slipped and issued financial guidance below analyst expectations. While CEO Rob Mee sought to reassure analysts by highlighting the upcoming Kubernetes integrations as an opportunity to expand Pivotal’s addressable market, investors at the time viewed the company’s trajectory as too slow relative to the broader industry shift toward Kubernetes-native platforms.

For DevOps teams already invested in the Cloud Foundry ecosystem, these announcements represented a pragmatic path forward: retaining familiar developer workflows like cf push while gaining the scheduling, portability, and ecosystem benefits of Kubernetes. Whether the alpha-stage products would mature fast enough to satisfy both developers and investors remained the open question in mid-2019.

Summary of Pivotal’s July 2019 Kubernetes Announcements

Why Root User Access Keys Are a Security Risk

Every AWS account has a root user — the original identity created when you first register with Amazon Web Services. The root user has unrestricted access to every resource and service within the account, bypassing any IAM policies or permission boundaries. If root user credentials are compromised, an attacker has complete control over your infrastructure, billing, and data.

AWS best practices strongly discourage using root user credentials for everyday operational tasks. Instead, you should:

• Create an IAM user with administrative privileges for day-to-day work.
• Enable multi-factor authentication (MFA) on the root account.
• Avoid creating long-lived root access keys unless absolutely necessary.
• Rotate or delete any existing root access keys that are no longer needed.

That said, there are edge-case scenarios where a root access key is required — for example, certain account-level API calls that only the root user can make. In those cases, the safest approach is to generate a new access key, perform the required task, and then immediately delete the key.

Two Complementary Strategies for Securing Root Access

Securing high-level AWS access effectively involves combining two approaches:

1. Create an administrative IAM user and group. Grant this IAM user full administrative privileges via an attached policy (such as AdministratorAccess). Use this identity for all routine administrative work instead of logging in as root.
2. Rotate or delete root access keys. Generate new access keys for the root user if required, then delete the old keys. If no root API access is needed at all, delete all root access keys entirely — this is the AWS-recommended state.

How to Create an Access Key for the AWS Root User

Follow these steps to create a new access key using the AWS Management Console. Note that you must be logged in as the root user to complete this process.

Step 1: Log in as the Root User

Navigate to the AWS Management Console. On the sign-in page, choose Root user and enter the email address associated with your AWS account, then enter your root password.

If you normally sign in with an IAM user, look for the option to switch to the root user sign-in method on the login screen.

Step 2: Open Security Credentials

Once logged in, click your account name or alias in the top-right navigation bar. A dropdown menu will appear. Select Security credentials (previously labelled My Security Credentials in older console versions).

Step 3: Navigate to Access Keys

On the Security credentials page, scroll down to the Access keys section. This section displays any existing access keys associated with the root user, along with their status (active or inactive) and the date they were last used.

Step 4: Create a New Access Key

Click the Create access key button. AWS will generate a new Access Key ID and Secret Access Key pair. You will see both values displayed on screen.

Important: The Secret Access Key is only shown once. Copy it immediately and store it securely (for example, in a secrets manager or encrypted vault). If you lose it, you cannot retrieve it — you will need to create a new key pair.

You can also download the key pair as a .csv file using the provided option on the same screen.

Step 5: Delete the Old Access Key

After confirming your new access key works as expected (by making a test API call), return to the Access keys section and delete the old key. Select the old key and click Delete. AWS will ask you to confirm by typing the word delete or the key ID — follow the prompt.

If you are unsure whether the old key is still in use by any automated processes, you can first set it to Inactive rather than deleting it immediately. Monitor for failures, then delete once you are confident nothing depends on the old key.

Best Practice: Delete All Root Access Keys

AWS explicitly recommends that most accounts should have no root access keys at all. Nearly every administrative task can be performed by an IAM user or role with appropriate permissions. Removing root access keys entirely eliminates a significant attack surface.

Use the IAM dashboard in the AWS Console to check your account’s security status. A green check mark next to No root access keys confirms you are following this best practice.

Combining Root Key Rotation with IAM User Setup

For a complete security hardening approach, pair root key rotation with the following IAM steps:

• Create an IAM user with the AdministratorAccess managed policy attached.
• Create an IAM group (e.g., Administrators) with the policy attached at the group level, then add the IAM user to that group.
• Enable MFA on both the root account and any IAM user with elevated privileges.
• Use IAM roles for programmatic access from services, Lambda functions, EC2 instances, and CI/CD pipelines — avoid embedding long-lived access keys in application code or configuration files.

Summary

Creating and rotating root user access keys in AWS is a straightforward process through the Security Credentials page in the Management Console. However, the most secure posture is to avoid root access keys altogether. Use IAM users, groups, and roles for all operational access, enable MFA on the root account, and treat the root user as a break-glass emergency identity only.

For further reading, refer to the official AWS documentation on root user best practices.

Why Cloud Characteristics Matter

Cloud computing has an ever-expanding catalogue of tools, services and deployment models, but its foundational characteristics have stayed remarkably consistent since AWS began selling compute and storage instances in 2006. Google and Microsoft followed shortly after, and today the cloud spans IaaS, PaaS and SaaS — encompassing AI, containers, serverless compute, managed databases, IoT, analytics and much more.

Each service category carries its own trade-offs, but seven core characteristics underpin all of them. Understanding these traits helps DevOps and platform engineering teams make informed architectural decisions, negotiate contracts and set realistic expectations with stakeholders.

1. On-Demand Computing

AWS, Microsoft Azure, Google Cloud and other public cloud platforms make compute and storage resources available at the click of a button or a single API call. Providers maintain massive pools of capacity across globally distributed data centres, ready to serve requests in seconds.

This is a fundamental departure from traditional IT procurement, where acquiring a new server could require months of approval cycles, purchasing, shipping and rack-and-stack work. On-demand access collapses that timeline to minutes, enabling rapid experimentation and faster time-to-market.

2. Self-Service Provisioning

On-demand computing is only useful if teams can access resources without raising a ticket and waiting for an operations team to act. Self-service provisioning — typically delivered through a cloud provider’s web console, CLI or infrastructure-as-code tooling — gives developers the ability to select the resources they need and start building immediately.

In practice, a platform or cloud-ops team sets guardrails through IAM policies, service quotas and organisational policies. Within those boundaries, development teams have the autonomy to build, test and deploy without bottlenecks. This self-service model is a cornerstone of modern DevOps culture, enabling teams to own the full lifecycle of their services.

3. Resource Pooling

Public cloud providers use multi-tenant architectures to serve many customers simultaneously from the same physical hardware. Customer workloads are abstracted from the underlying infrastructure through hypervisors, container runtimes and software-defined networking, so that multiple tenants share a host without visibility into each other’s data or operations.

Providers increasingly design custom silicon and abstraction layers — such as AWS Nitro, Google Titanium or Azure’s FPGA-based offloads — to improve both performance and the security boundaries between tenants. Resource pooling is what makes cloud economics work: providers achieve high hardware utilisation while offering individually elastic allocations to each customer.

4. Scalability and Elasticity

Because resources are pooled, they can also be reallocated dynamically. Compute, storage and networking capacity can be added or removed in response to real workload demand rather than forecast estimates. Cloud platforms support two primary scaling axes:

• Vertical scaling (scaling up/down): Increasing or decreasing the CPU, memory or disk of an existing instance.
• Horizontal scaling (scaling out/in): Adding or removing instances behind a load balancer, often managed by an autoscaling group or Kubernetes horizontal pod autoscaler.

By contrast, traditional on-premises architectures force teams to provision for peak capacity. Resources sit idle during quiet periods, wasting capital and operational spend. Elasticity removes this requirement — you pay for what you use and scale back when demand drops.

5. Pay-Per-Use Pricing

Cloud pricing shifts IT expenditure from capital expenditure (Capex) — buying hardware up front — to operational expenditure (Opex), paying only for resources consumed. Most providers offer per-second or per-hour billing for compute, with similar granularity for storage, data transfer and managed services.

While pay-as-you-go is an advantage, it introduces a new risk: uncontrolled spend. DevOps teams need to practise FinOps disciplines to manage costs effectively:

• Right-size instances to avoid over-provisioning.
• Shut down or scale down resources when not in use (e.g., development environments overnight).
• Use reserved instances, savings plans or committed-use discounts for predictable baseline workloads.
• Set budget alerts and cost anomaly detection to catch runaway spend early.

Providers now offer a range of pricing models beyond pure on-demand billing — including reserved capacity, spot or preemptible instances and sustained-use discounts — giving teams flexibility to optimise cost for their specific usage patterns.

6. Resiliency and High Availability

Cloud providers architect their platforms to minimise single points of failure. Key resilience constructs include:

• Availability Zones (AZs): Physically separate data centres within a geographic region, connected by redundant low-latency networks. Distributing workloads across multiple AZs protects against a single facility failure.
• Regions: Geographically isolated deployments that can be used for disaster recovery or data residency requirements.
• Managed service redundancy: Many higher-level services (managed databases, object storage, load balancers) automatically replicate data or distribute traffic across AZs without user configuration.

That said, no platform is immune to outages. Major providers have experienced significant incidents that affected entire regions or service categories. Teams should design for failure by:

• Implementing multi-AZ deployments for critical workloads.
• Testing failover procedures regularly (chaos engineering).
• Considering multi-region or even multi-cloud strategies for the highest availability requirements, accepting the added complexity and cost that entails.

7. Security and the Shared Responsibility Model

Security in the cloud operates under a shared responsibility model. The cloud provider secures the underlying infrastructure — physical data centres, hypervisors, global network fabric and managed service platforms. The customer is responsible for everything they build and configure on top: operating system hardening, application security, identity and access management, data encryption and network controls.

Major cloud providers invest heavily in security expertise, compliance certifications and threat intelligence — often beyond what individual enterprises can replicate on-premises. However, the most common cloud security incidents are not provider-side breaches; they are misconfigurations by customers, such as publicly exposed storage buckets, overly permissive IAM roles or unpatched application code.

To operate securely in the cloud, DevOps teams should:

• Understand clearly where provider responsibility ends and customer responsibility begins for each service used.
• Apply the principle of least privilege to all IAM policies and service roles.
• Enable cloud-native security tooling (e.g., AWS Security Hub, Azure Defender, Google Security Command Center).
• Integrate security scanning into CI/CD pipelines to catch misconfigurations before they reach production.
• Encrypt data at rest and in transit using provider-managed or customer-managed keys as appropriate.

Putting the Characteristics Together

These seven characteristics — on-demand access, self-service provisioning, resource pooling, scalability, pay-per-use pricing, resiliency and shared security — are not independent features. They are interdependent properties that, together, define what cloud computing is and why it has become the default platform for modern application development and operations.

For DevOps teams, internalising these characteristics shapes architectural decisions: designing stateless, horizontally scalable services; automating infrastructure provisioning through code; implementing FinOps practices to control spend; building for failure; and treating security as a shared, ongoing engineering discipline rather than a one-time checkbox.

This accelerated cadence lets organisations adapt to market shifts, respond to user feedback faster, and keep pace with competitors. Most importantly, it enables businesses to continuously deliver value to the people who use their products. The mechanism that makes this possible at scale is DevOps.

The Hidden Enemy: Wasted Time Between Phases

Ask most engineers which phase of the software development life cycle (SDLC) takes the longest, and they will name development, testing, or deployment. The honest answer is none of those. The greatest time sink is not inside any single phase — it is the gaps between phases.

• Developers wait on business analysts to deliver requirements.
• Testers wait on developers to finish code.
• Developers wait on testers to confirm whether the code works.
• Everyone waits on system administrators to provision environments or push releases.
• Sysadmins wait on everyone else to diagnose why a deployment failed.

In many organisations, a substantial portion of engineering effort goes into activities that produce no direct value for the customer. Waiting in queues delivers nothing. Manually re-running the same regression suite for every release delivers nothing new. Submitting a ticket to a separate deployment team when the application team could run a single command themselves delivers nothing that justifies the overhead. Yet all three practices are commonplace.

DevOps directly addresses this problem by compressing or eliminating idle time through automation and by collapsing the handoff points that create queues in the first place.

Breaking Down Silos: DevOps as a Cultural Shift

In a traditionally structured organisation, the answer to “who is responsible for this product?” is usually “nobody in particular.” Every department owns a slice — development, QA, operations, security — but no single team is accountable end-to-end. This fragmentation has predictable consequences:

• Developers may not know how their application is tested or deployed, and may not consider those concerns when writing code.
• Sysadmins may be unaware of how much friction their ticketing process creates for developers trying to ship code.
• QA teams may be disconnected from production monitoring, missing signals that would inform their test strategies.

No single team sees the complete life cycle of an application. Because responsibility is partitioned, empathy between teams erodes. Communication becomes transactional — a Jira ticket here, an email there — rather than collaborative.

DevOps tackles this disconnect head-on. Its core premise is cultural: unite the people involved in an application’s life cycle into a single, autonomous team that owns everything from requirements and development through testing, deployment, monitoring, and on-call response. When one team holds end-to-end accountability, several things change:

• Handoffs disappear. There is no separate department to hand a release to, so inefficient escalations are eliminated.
• Empathy grows. When developers are also on the pager when something breaks at 2 a.m., they write more resilient code. When testers pair with developers during feature work, defects are caught earlier.
• Ownership is clear. A single product owner for the team means priorities are aligned rather than negotiated across departmental boundaries.

What DevOps Is Not

A common misunderstanding is that DevOps means creating a new department labelled “DevOps” or hiring a set of engineers with “DevOps” in their job titles. This approach reproduces the very silo it claims to dissolve. A DevOps team sitting alongside separate development and operations teams still generates the same handoff friction — it just has a different name on the door.

DevOps is not a role, a tool, or an organisational layer. It is a set of cultural and engineering practices that allow cross-functional teams to own the full life cycle of the software they build. Tooling — CI/CD pipelines, infrastructure as code, automated testing, observability platforms — is essential, but it is in service of the cultural goal, not the goal itself.

Automation: Reclaiming Time for Work That Matters

Eliminating idle time and automating repetitive tasks does more than speed things up — it frees cognitive bandwidth. When engineers are not manually shepherding deployments or babysitting regression suites, they can direct that attention toward solving real problems and building new capabilities.

Key automation practices that underpin a DevOps approach include:

• Continuous Integration (CI): Automatically build and test every code change as it is committed, catching regressions within minutes rather than days.
• Continuous Delivery/Deployment (CD): Automate the path from a passing build to a production release, making deployments repeatable and low-risk.
• Infrastructure as Code (IaC): Define and provision environments programmatically, eliminating manual configuration and environment drift.
• Automated Testing: Replace repetitive manual test cycles with comprehensive automated suites that run on every change.
• Observability: Instrument applications for logging, metrics, and tracing so teams can detect and respond to issues without waiting for user reports.

The Three Ingredients for Continuous Customer Value

Taken together, the DevOps approach produces three compounding advantages:

1. Elimination of wasted time and resources. Automating repetitive tasks and removing unnecessary handoffs returns engineering hours to work that directly benefits the customer.
2. Improved collaboration. Cross-functional teams with shared accountability build better software because every member understands the downstream consequences of their decisions.
3. Decentralised innovation. When teams are empowered to own their full stack, experimentation happens closer to the problem. Good ideas do not have to travel through layers of management to reach production.

These three factors shift an organisation from a reactive posture — trying to keep up with competitors — to a proactive one, where teams have the capacity and confidence to tackle today’s challenges and anticipate tomorrow’s.

Summary

DevOps is fundamentally about reducing the distance between an idea and its delivery to the customer. It does this by eliminating the idle time that accumulates between SDLC phases, dismantling the organisational silos that create those gaps, and automating the repetitive work that consumes engineering capacity without producing customer value. When the cultural and technical foundations are in place, teams ship faster, systems are more reliable, and businesses can focus their energy on genuine innovation rather than internal coordination.

Original source: cloudcomputing-news.net (2019)

The Rise of Microservices in the Enterprise

Microservices have moved well beyond early-adopter circles. Database development and management are changing significantly as organizations at scale migrate away from monolithic architectures toward fine-grained, independently deployable services. The shift is not happening in isolation: it arrives alongside Kubernetes, software containers, DevOps practices, cloud computing, domain-driven design (DDD), and NoSQL databases—all of which share a common goal of getting new applications into production faster and updating them more frequently.

Microservices bear a conceptual resemblance to the earlier service-oriented architecture (SOA) movement. Both aim to decompose large, tightly coupled systems into smaller, more manageable units. The key difference is that microservices lean much harder into independent deployment, decentralized data ownership, and fine-grained service boundaries defined by business domains.

Industry Signals: M&A Activity Points to Microservices’ Influence

The architectural shift has had observable effects on the industry’s deal-making. The move to microservices—and the container and orchestration ecosystem around it—was cited as a contributing factor in IBM’s plans to acquire Red Hat, as well as in the merger of Hadoop ecosystem vendors Cloudera and Hortonworks. When large incumbents reorganize around a technology trend, it is a strong signal that the trend has reached mainstream enterprise relevance.

Domain-Driven Design: Business Problems First, Technology Second

Zhamak Dehghani, who spent roughly 20 years working as a software engineer and architect in distributed computing, embedded systems, and communications before joining ThoughtWorks, has focused her consulting work on applying domain-driven design (DDD) concepts to operational systems.

The DDD approach inverts a common anti-pattern. Rather than selecting a technology stack first and then bending a business problem to fit it, teams start by deeply understanding the business domain and let that understanding drive architectural and technology decisions. In practice, Dehghani explained, this has pushed organizations toward NoSQL databases that are optimized for specific access patterns, rather than relying on a single general-purpose relational database to serve every use case. ThoughtWorks contributed to this shift, helping drive adoption of purpose-fit data stores and, more recently, low-latency globally distributed SQL databases.

Dehghani also contributes to the ThoughtWorks Technology Radar, a regularly updated technology assessment that many engineering teams use as a reference when evaluating open-source tools and architectural patterns, including microservices options.

From Monoliths to Microservices: Decentralizing Database Ownership

One of the most consequential organizational changes driven by microservices adoption is the movement of database responsibility away from a centralized DBA team and toward the developers embedded in individual business domains.

“Businesses used to put the developers in one tier and the users in another. Now, as you see a move from monoliths to microservices, you see a change in how we organize IT. What used to happen was that operational data was owned by DBAs or database experts that integrated all the applications into one layer of a database.”

— Zhamak Dehghani, Principal Consultant, ThoughtWorks

The old model created a predictable bottleneck. Every schema change, every new data requirement, meant filing a request with a separate team and joining a queue. Developers lost velocity; business units lost agility.

“With microservices, we have changed the model so that the people that own the application or service are also responsible for the database, the data itself, and the schema in which it resides.”

— Zhamak Dehghani

This is a fundamental shift in how data governance works day-to-day: accountability moves to the team closest to the domain, reducing handoff friction and making the feedback loop between software behavior and data model much tighter.

The Big Data Gap: Why Analytical Workloads Lagged Behind

While microservices transformed operational data management, big data and analytical workloads largely developed along a separate, more monolithic track. Open-source frameworks like Hadoop and Apache Spark represented real progress away from proprietary systems, but Dehghani argued that they were mostly implemented as centralized, monolithic platforms rather than domain-aligned services.

The consequences have been practical and painful: these big data projects have been difficult to move into general production, and, in her assessment, they often failed to fully meet organizational needs. Analytical and machine learning data pipelines remained, as she put it, “centralized and disconnected from the business domains” that generated the underlying data.

Data lakes—once the defining artifact of enterprise big data strategy—have frequently devolved into data silos: large, ungoverned repositories where data enters but value struggles to exit.

Data Mesh: Applying Microservices Thinking to Big Data

Dehghani outlined an alternative in a post on Martin Fowler’s widely read technology blog (martinfowler.com), where she introduced the concept of the data mesh—a distributed data architecture paradigm that applies domain-driven, decentralized ownership principles to analytical data, much as microservices did for operational data.

The core idea: instead of funneling all analytical data into a central lake or warehouse owned by a data engineering team, each business domain owns and serves its own analytical data as a product. Consumers—other domains, data scientists, ML pipelines—access domain data through well-defined interfaces, just as they would call a microservice API.

“The way we broke down operational capabilities around domains has to come to big data, too.”

— Zhamak Dehghani

What a Successful Convergence Requires

Dehghani identified several prerequisites for microservices and big data to converge effectively in practice:

• Infrastructure abstraction: Individual application teams should not be expected to own and maintain the infrastructure their services run on. Cloud providers and platform/infrastructure teams must provide self-service capabilities that abstract away that complexity.
• Organizational maturity: The transition is a journey, not a weekend project. Teams that attempt to run microservices on cloud infrastructure overnight without building foundational skills and processes tend to fail.
• Incremental adoption: Dehghani specifically cautioned against big-bang migrations. Many failures she observed were attributable to organizations not recognizing that the architectural shift requires an incremental approach, with skills and processes maturing in step with the architecture.
• Respecting existing skill sets: The path an organization takes must account for what its teams already know. Copying another organization’s microservices playbook without adjusting for your own capability gaps is a recipe for trouble.

“Respecting organization maturity is important. I see a lot of people that want to run microservices on the cloud overnight. But that change is a journey in itself. A lot of failures I see are due to not recognizing that—not having an incremental approach.”

— Zhamak Dehghani

Key Takeaways for Data and DevOps Engineers

• Microservices shift database ownership from centralized DBA teams to domain-aligned development teams—a significant governance and organizational change, not just a technical one.
• Big data and analytical architectures are beginning to follow the same decentralization path, with the data mesh pattern offering a concrete model.
• Data lakes without domain ownership tend to become silos. Domain-aligned data products are the proposed antidote.
• Kubernetes, containers, cloud platforms, DDD, and NoSQL are all part of the same wave. Understanding how they reinforce each other is essential for architects designing modern data platforms.
• Incremental adoption and honest assessment of organizational maturity are more reliable predictors of success than raw ambition or technology choices alone.

The Data Engineering Bottleneck Slowing AI Adoption

Data integration and big data management solutions provider Attunity announced an analytics platform designed to automate streaming data pipelines for AI, machine learning, and data science initiatives. The core problem it addresses is a familiar one: data engineering teams cannot keep pace with the demand for real-time datasets that machine learning applications require.

Data integration and migration are often manual, complex, and time-consuming processes. By the time a dataset is assembled and delivered to data scientists, it is frequently outdated — undermining the value of the analysis before it even begins. Attunity’s approach targets this lag directly by combining efficient change data capture (CDC) at scale with automated data transformations running on Apache Spark.

Attunity for Databricks Unified Analytics Platform

The integration — Attunity for Databricks Unified Analytics Platform — provides continuous change data capture, delivery, and automated data refinement to produce analytics-ready datasets inside Databricks. Databricks’ Unified Analytics Platform unifies data processing and machine learning in a single environment, with Apache Spark as the high-performance processing engine underneath.

By pairing Attunity’s CDC pipeline automation with Databricks, organizations gain a path from raw transactional source data to clean, ML-ready datasets without writing custom integration code. This matters in practice because manual pipeline development is a significant time sink for data engineers — time that could otherwise be spent on higher-value work.

“Our ability to reach into virtually all enterprise systems and deliver real-time, analytics-ready and transactionally consistent data without coding is a powerful enabler for data scientists, allowing them to generate the business insights that accelerate corporate decision making and growth.”

Itamar Ankorion, Chief Marketing Officer, Attunity

Key Capabilities

Accelerated Time to Value

Using Apache Spark as the processing engine, Attunity automates data delivery and creates analytics-ready datasets in Databricks. Data engineers can build reusable, automated pipelines that reduce reliance on manual coding and expensive custom development resources, shortening the delivery cycle from source systems to data scientists.

Reduced Data Preparation Overhead

Attunity’s pipeline automation reduces the data preparation burden on data scientists. Rather than spending significant time wrangling raw data, data scientists can focus on analysis and model development. More agile pipeline generation means that changes to source data schemas or business requirements can be accommodated without rebuilding pipelines from scratch.

Real-Time Data with CDC at Scale

Attunity’s change data capture (CDC) technology provides the low-latency, high-throughput characteristics needed for large-scale machine learning datasets in the cloud. CDC captures only the changes made to source data — inserts, updates, and deletes — and streams those changes continuously, rather than running expensive bulk extractions on a schedule.

Multi-Cloud Support

Attunity supports Databricks deployments on both Microsoft Azure and Amazon Web Services (AWS), as well as a broad range of data lake, data warehouse, and streaming services available on those platforms. This gives organizations flexibility to adopt the platform within their existing cloud strategy without vendor lock-in to a single cloud provider.

Transactionally Consistent, ACID-Compliant Data

Attunity provides full support for the Databricks Unified Analytics Platform’s ACID capabilities. Transactions are delivered and applied in the order they were committed on the source system, ensuring that data scientists and ML models work from a transactionally consistent view of the data — a critical requirement for models where data ordering and integrity affect accuracy.

Why This Matters for DataOps

The Attunity-Databricks integration reflects a broader shift toward DataOps practices — applying DevOps-style automation, collaboration, and continuous delivery principles to data pipelines. Automating the movement and transformation of data reduces the manual coordination between data engineering and data science teams, shortens feedback loops, and makes it easier to keep ML models trained on current data.

For enterprises with data spread across siloed storage systems — on-premises databases, cloud data warehouses, streaming platforms — the ability to unify those sources into a single analytics environment without hand-coded ETL is a meaningful operational improvement. The combination of CDC for real-time ingestion and Spark for transformation automation addresses the two most time-consuming stages of the modern data pipeline.

Note: Attunity was acquired by Qlik in May 2019, shortly after this announcement. Organizations evaluating this technology should review current product branding and availability under Qlik.

Why Big Data Still Matters

According to an Accenture study cited in the original source, 79% of enterprise executives believe that companies not embracing big data will lose their competitive edge, and 83% say their organizations have pursued big data projects to stay ahead. Despite this widespread awareness, actual success rates remain low: a Capgemini survey found that only 27% of executives described their big data initiatives as successful.

The gap between ambition and execution is a persistent problem. Most businesses know they should be leveraging big data, but stumble when it comes to implementation. With data volumes continuing to grow rapidly, the ability to process data quickly and derive meaningful insight from it is becoming a core operational requirement rather than a differentiator.

What Is Big Data?

“Big data” refers to extremely large datasets that are too voluminous, varied, or fast-moving for traditional data processing tools to handle effectively. The value of big data lies not in the raw volume, but in the ability to analyze it for patterns, trends, and correlations that inform better business decisions.

Big data is commonly characterized by three dimensions:

• Volume: Datasets ranging from terabytes to petabytes and beyond.
• Velocity: Data arriving in real time or near-real time from sources such as sensors, logs, and user interactions.
• Variety: Structured, semi-structured, and unstructured data from databases, documents, images, social media, and more.

The increasing diversity of data sources is what makes modern big data analytics particularly powerful — and particularly challenging to implement well.

Engines vs. Frameworks: Understanding the Terminology

Two terms that come up constantly in big data discussions are processing engines and frameworks. While the distinction is not always rigid, a useful working definition is:

• Processing engine: The core component responsible for executing operations on data — sorting, filtering, aggregating, or transforming it.
• Framework: A broader set of components, libraries, and conventions that work together to manage the full data processing lifecycle, including ingestion, storage, processing, and output.

Both share the same fundamental goal: operate over large amounts of data to surface patterns, broaden understanding, and expose complex interactions that would be invisible in smaller samples.

Why Cloud Infrastructure Is Central to Big Data

On-premises hardware can handle big data workloads, but the capital cost and operational overhead of building out that infrastructure at scale is prohibitive for most organizations. Cloud environments address this by providing:

• Elastic scalability: Compute and storage resources can expand to meet peak workloads and contract when demand drops, reducing waste.
• Managed services: Cloud providers offer managed versions of popular big data tools, reducing the operational burden of cluster management and patching.
• Cost model: Pay-as-you-go pricing allows organizations to run large batch jobs without permanently provisioning the underlying infrastructure.
• Integration: Modern cloud platforms offer tight integration between data storage, processing, and business intelligence (BI) tooling, streamlining the analytics pipeline.

The combination of cloud scalability with big data frameworks is what has made large-scale analytics accessible to organizations outside the technology sector.

Key Big Data Processing Frameworks

Apache Hadoop

Hadoop is a Java-based open-source framework under the Apache Software Foundation umbrella. It is designed to store and process very large datasets across clusters of commodity hardware using a distributed computing model. Its core components include:

• HDFS (Hadoop Distributed File System): Distributes data across cluster nodes with built-in replication for fault tolerance.
• MapReduce: The original Hadoop processing model, inspired by Google’s MapReduce paper, which breaks large jobs into parallel map and reduce tasks.
• YARN (Yet Another Resource Negotiator): Manages cluster resources and job scheduling.

Hadoop can be deployed on-premises in an organization’s own data center or run on cloud infrastructure. Its open-source nature and large ecosystem of compatible tools have driven widespread industry adoption.

Apache Spark

Apache Spark is a fast, general-purpose distributed processing engine that addresses some of Hadoop MapReduce’s limitations, particularly for iterative workloads. Key characteristics include:

• In-memory processing: Spark can cache intermediate data in memory across iterations, making it significantly faster than disk-based MapReduce for many workloads.
• Unified API: Spark supports batch processing, streaming (Spark Streaming / Structured Streaming), SQL queries (Spark SQL), machine learning (MLlib), and graph processing (GraphX) within a single framework.
• Language support: APIs are available in Scala, Java, Python, and R.

Apache Storm is another open-source option in this space, focused specifically on real-time stream processing rather than batch workloads.

Cloudera Data Platform

Cloudera provides a commercial distribution and managed platform built on open-source technologies including Apache Hadoop, Apache Spark, Apache Hive, Apache Kafka, and others. It is designed to simplify the discovery, storage, processing, modeling, and serving of large amounts of data, adding enterprise features such as security, governance, and support on top of the upstream open-source projects.

Hadoop and Apache CloudStack: An Open-Source Combination

Apache CloudStack is an open-source cloud computing platform used to deploy and manage large networks of virtual machines. Like Hadoop, it is implemented in Java and is part of the Apache Software Foundation. For organizations that prefer to build their own private cloud infrastructure rather than rely on public cloud providers, CloudStack offers a fully open-source path.

Running Hadoop on a CloudStack-managed private cloud gives organizations the operational flexibility of cloud-style resource management — elastic scaling, self-service provisioning, and multi-tenancy — while keeping data and compute on infrastructure they control. This pairing is particularly relevant for industries with strict data residency or regulatory requirements that make public cloud options complicated.

Turning Big Data Ambition into Execution

The statistics make clear that awareness of big data’s value is nearly universal among enterprise leaders. What separates successful organizations is execution: choosing the right framework for the workload, building the infrastructure to support it reliably, and ensuring the output connects to decisions that actually improve the business.

A practical starting point for most teams is to:

1. Define the business question the data analysis should answer before selecting a tool.
2. Start with a manageable proof-of-concept dataset rather than attempting to process everything at once.
3. Evaluate managed cloud services (such as Amazon EMR, Google Dataproc, or Azure HDInsight) before committing to self-managed cluster infrastructure.
4. Invest in data quality and pipeline reliability — bad input data produces unreliable insights regardless of how powerful the processing engine is.
5. Build feedback loops so that analytical outputs visibly influence decisions, creating organizational buy-in for further investment.

Big data processing is not inherently complex, but it does require deliberate architectural choices and a clear connection between the data being collected and the outcomes the organization is trying to improve.

What Has Changed in Modern Application Environments?

Contemporary applications are rarely monolithic. They are built on containers, microservices, cloud services, and fine-grained compute and storage functions that form a constantly shifting mesh of dependencies. When performance degrades, the root cause could be hiding anywhere across thousands of objects, methods, and transactions per second — in the application code, the network, the infrastructure, or even the end user’s device.

Traditional monitoring approaches — dashboards, threshold alerts, manual log reviews — struggle to keep up with this level of complexity and dynamism. The combination of big data collection and machine learning analysis offers a more effective path: gather comprehensive transaction data and apply automated pattern recognition to identify and classify critical anomalies and bottlenecks at scale.

Why Machine Learning Requires Big Data

More data produces better machine learning models. When teams or tools rely on sampled data — say, one metric reading per minute or per five minutes — they risk missing short-lived outliers entirely, or misrepresenting them through averaging. In a sub-second world, a five-minute sampling interval is practically blind.

Effective APM machine learning requires collecting system and resource data continuously, from all applications, all devices, and all individual transactions. This high-fidelity data is essential for training algorithms on real-world behavior rather than on statistically smoothed approximations. If the training data misses edge cases, the model will too — and those edge cases are often precisely where the most impactful performance problems hide.

Data quality matters. It is important to collect all relevant metrics and associated metadata before applying machine learning, rather than relying on sampling algorithms that may miss issues in dynamic application environments.

How APM Machine Learning Models Are Trained

An APM machine learning engine builds its models by analyzing large volumes of transaction data to isolate patterns, clusters, and correlations. Training typically starts with known scenarios: subject-matter experts provide labeled examples of common application performance problems, and the model learns the signatures associated with each category.

Because no single organization can provide a sufficiently broad range of training scenarios on its own, APM tools often train initial models on anonymized, real-world data sets drawn from organizations across multiple industries. Experts then review the model’s classifications and correct errors, enabling the system to refine its understanding iteratively.

Training is an iterative process:

1. Isolate a set of transactions sharing a common characteristic.
2. Remove those transactions from the data set.
3. Evaluate whether the performance issue persists in the remainder.
4. Repeat, progressively narrowing the probable root cause.

This process allows the model to surface unanticipated issues that were not part of the original training scenarios. The richer the metadata attached to each transaction, the more accurately the system can pinpoint patterns and causes.

A Practical APM Example: Analyzing 10,000 Transactions

Consider a chart plotting detailed response-time data collected from 10,000 individual transactions. To a human analyst, it looks like a roughly normal distribution with a cluster of outliers. Manually sorting those outliers into meaningful categories — web service timeouts, database timeouts, authorization failures, initialization errors — could take a DevOps or QA team weeks or months, well after the underlying issues have already impacted users.

A machine learning model can perform the same grouping in minutes. It iteratively clusters and re-clusters outliers, examines interdependencies, isolates commonalities, and surfaces the specific methods, database calls, servers, or other characteristics consistently associated with slow transactions. Rather than guessing or relying on anecdote, the system makes decisions based on statistical evidence drawn from the full data set.

An important insight that emerges from this kind of analysis: not all slow transactions are slow for the same reason. Machine learning can separate distinct performance problems that would appear identical in aggregate metrics, enabling teams to address each root cause with the appropriate fix.

How Machine Learning Helps DevOps Teams Day-to-Day

With a solid big-data foundation, machine learning APM systems deliver several practical benefits to DevOps teams:

• Multi-source correlation: Systems can synchronize server statistics, log file entries, and individual transaction records using shared metadata, giving teams a unified view of an incident instead of isolated data silos.
• Impact prioritization: By quantifying how many users and transactions are affected by a given anomaly, machine learning helps teams focus effort where it will have the greatest business impact.
• Release comparison: Historical transaction data enables rapid before-and-after comparisons across deployments, making it easier to detect regressions and validate that a fix actually worked.
• Visualization of complex dependencies: Machine learning can transform intricate interdependency graphs into visual representations that humans can interpret far more quickly than tables of raw metrics.
• Faster drill-down: When anomalous transactions are identified, the system can guide engineers directly to the offending method, server, or network segment rather than requiring manual log trawling.

Key Takeaways for DevOps Engineers

Machine learning is not a silver bullet, but applied thoughtfully to APM it addresses a genuine gap: the inability of human teams to process the volume, velocity, and variety of telemetry data produced by modern distributed applications. The practical outcomes include faster identification of performance issues, reduced bug rates when integrated into development workflows, and improved user satisfaction.

The prerequisite for all of this is data quality. Sampled or averaged metrics undermine the training process and reduce model accuracy. Collecting complete, high-resolution, metadata-rich telemetry from every transaction is not optional — it is the foundation on which effective APM machine learning is built.

Original article sourced from devopsonline.co.uk (2018).