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How Amazon grew an awkward side project into AWS, a behemoth that’s now 4 times bigger than its original shopping business

If you have touched your phone or computer today, you very likely have been touched by a vast business few outside the technology world are aware of. Maybe you’ve checked the Wall Street Journal or MarketWatch, traded a stock on Robinhood, bet on a football game through DraftKings , or posted on Pinterest or Yelp; ordered treats for Fido on Chewy or treats for yourself on DoorDash; submitted an expense report on Workday or made plans for the evening via Tinder, OkCupid, or Hinge. 

If so, you did it with the help of Amazon Web Services. The less glamorous sibling to Amazon’s operations in e-commerce, streaming video, and smart devices, AWS is no less ubiquitous, deploying millions of computers worldwide, humming away somewhere in the cloud.

For all those AWS customers the on-demand cloud computing platform isn’t just another vendor. They rely on it so heavily that it resembles a public utility—taken for granted, but essential to keep the machinery humming. In the past 12 months each of the companies mentioned above has stated in Securities and Exchange Commission filings that they “would be adversely impacted” if they lost their AWS service. Hundreds more companies— Netflix , Zoom , Intuit , Caesars Entertainment—have reported the same risk factor to the SEC in the past year. By the way, the SEC uses AWS. (So does Fortune .)

And those are but the tiniest fraction of AWS customers. AWS—initially run by Andy Jassy, who went on to succeed Jeff Bezos as Amazon’s CEO—won’t say how many customers it has, only that it provides computing power, data storage, and software to millions of organizations and individuals. Now, even as Amazon lays off a reported 10,000 workers, Wall Street analysts expect another blowout performance from its web services division. That’s probably why few if any of those staffing cuts will affect this relatively recession-proof part of Bezos’s empire. (Amazon won’t say how many of its 1.5 million employees work for AWS.)

For years AWS has brought in more profit than all other divisions of Amazon combined, usually by a wide margin. AWS’s operating profit last year, $18.5 billion, was nearly three times the operating profit reported by the rest of the company ($6.3 billion). AWS pulled in $58.7 billion of revenue in this year’s first nine months; if it were independent, it would easily rank in the Fortune 100.  

How did this offshoot of an online retailer come to rule the lucrative cloud-computing industry, towering over tech giants such as Microsoft and Google , which might have seemed better positioned to dominate?

AWS’s ascent is so unlikely that it demands an explanation. It reveals the power of a truly iconoclastic culture that, while at times ruthless, ultimately breeds innovation and preserves top talent by encouraging entrepreneurship.

The best-known origin story of AWS is that it started when Amazon had some spare computer capacity and decided to rent it out to other companies. That story won’t die, but it isn’t true. The real story traces a circuitous path that could easily have ended in a ditch. It’s grounded in a philosophy that still guides AWS’s progress.

“To me, it’s the concept of insurgents versus incumbents,” says Adam Selipsky, who became AWS’s CEO last year when his predecessor, Jassy, took over as Amazon CEO. Selipsky, 56, speaks quietly, conveying an understated intensity. “One thing that I think is really important, that we intentionally worry about all the time,” he says, is that “we continue to keep the customer need dancing in front of our eyes at all times.”

The real story of the AWS insurgency began with Amazon’s innovative responses to two problems. First: By the early 2000s, Amazon—still known mainly as an online bookseller—had built from scratch one of the world’s biggest websites, but adding new features had become frustratingly slow. Software engineering teams were spending 70% of their time building the basic elements any project would require—most important, a storage system and an appropriate computing infrastructure. Building those elements for projects at Amazon scale was hard, and all that work merely produced a foundation on which to build the cool new customer-pleasing features Amazon was seeking. Every project team was performing the same drudgery. Bezos and other Amazon managers started calling it “undifferentiated heavy lifting” and complaining that it produced “muck.” 

In response, Selipsky recalls, company leaders began to think, “Let’s build a shared layer of infrastructure services that all these teams can rely on, and none of them have to spend time on general capabilities like storage, compute capabilities, databases.” Amazon’s leaders didn’t think of it as an internal “cloud”—the term wasn’t widely used in the tech world yet—but that’s what it was.

The second problem involved other websites wanting to add links to Amazon products on their own pages. For example, a website about cooking might recommend a kitchen scale and include a link to the Amazon.com page for the product. Amazon was all for it, and would send them a bit of code they could plug into their site; if someone bought the product through the link, the site owner earned a fee. But as the program grew, cranking out bits of code for every affiliate site became overwhelming, and those affiliates’ website developers wanted to create their own links and product displays instead of the ones Amazon sent them. So in 2002 Amazon offered them a more advanced piece of software, enabling them to create far more creative displays. The new software was complicated. Users had to write software rather than just plug it in. Yet thousands of developers loved it immediately.

When Amazon launched a fuller, free version of the software building block a few months later, it enabled anyone, not just affiliates, to incorporate Amazon features into their sites. The surprise: A lot of the downloads were going to Amazon’s own software engineers. The building block turned out to be a proof of concept for the labor-lightening innovations that Amazon itself was looking for.

A picture was emerging. Amazon desperately needed to free its software developers from creating muck. Developers everywhere, not only its own, were starving for new tools that did just that. “We very quickly figured out that external developers had exactly the same problems as internal developers at Amazon,” Selipsky says.

But was that a business for Amazon? During a 2003 offsite at Bezos’s house, the company’s top managers decided that it could be. That decision was the turning point, especially significant because it could so easily have gone the other way. Amazon’s customers were consumers who bought “new, used, refurbished, and collectible items,” as the company told investors at the time. Why would anyone imagine this company could build a business selling technology services to software developers?

The decision to plunge ahead revealed a subtle distinction that outsiders didn’t understand. The world saw Amazon as an online retailer, but the company’s leaders never thought of it that way. They thought of it as “a technology company that had simply applied its technology to the retail space first,” Jassy later told Harvard Business School professors who were writing a case study. For that kind of company, AWS looked like a promising bet.

Coming out of the 2003 offsite, Jassy’s job was to build a team and develop AWS. He wrote a proposal for it as a cloud-computing business. The document, one of the famous six-pagers used at Amazon’s executive meetings instead of PowerPoint (which is banned), reportedly went through 31 revisions.

It took three years before AWS went live. In 2005 Jassy hired Selipsky from a software firm to run marketing, sales, and support, Selipsky recalls: “Amazon called and told me there was this initiative for something about turning the guts of Amazon inside out, but other companies could use it.” AWS’s first service, for data storage, “was such a novel concept that it was even hard to explain and hard for me to understand,” he says.

Wall Street didn’t get it. “I have yet to see how these investments are producing any profit,” a Piper Jaffray analyst said in 2006. “They’re probably more of a distraction than anything else.”

The rest of the world didn’t get it either. “I cannot tell you the number of times I got asked, with a quizzical look on people’s faces, ‘But what does this have to do with selling books?’ ” Selipsky recalls. “The answer, of course, was: AWS has nothing to do with selling books. But the technology we use to sell books has everything to do with AWS and what we can offer customers.” Those customers were software developers, an entirely new target market that baffled outsiders.

AWS was prepared for that reaction. One of Amazon’s principles reads in part: “As we do new things, we accept that we may be misunderstood for long periods of time.”

On the day in March 2006 when AWS finally launched its inaugural service—S3, for Simple Storage Service—Selipsky was at a trade show in Santa Clara, Calif., “in a windowless, internet-less conference room,” as he describes it, unable to learn how the launch was going. At day’s end he and a colleague ran outside to call Seattle for news. 

“We were told that 12,000 developers had signed up,” he says, a note of marvel still in his voice. “On the first day. It was just amazing.”

Five months later AWS launched its other foundational service, EC2, for Elastic Compute Cloud, which was also instantly popular. The revolution had begun. Instead of raising millions of dollars to buy servers and build data centers, startups could now get online with a credit card, and pay a monthly bill for just the computing power and storage they used. If their new app was a hit, they could immediately engage all the cloud services that they needed. If it bombed, they weren’t stuck with rooms of junk equipment. As a Silicon Valley entrepreneur and early AWS customer told Wired in 2008: “Infrastructure is the big guys’ most powerful asset. This levels the field.”

In response to that historic shift, AWS’s potential competitors did … nothing. “A business miracle happened,” Bezos told a conference years later. “This is the greatest piece of business luck in the history of business so far as I know. We faced no like-minded competition for seven years. I think the big established enterprise software companies did not see Amazon as a credible enterprise software company, so we had this incredible runway.”

Selipsky suspects an additional motivation: “They either didn’t believe this could be a real business, or they were so threatened by what it would do to their own business models, and the way they were overcharging customers, that they didn’t want to believe it.”

No one, not even at Amazon, foresaw how massive a business cloud computing would be, or AWS’s dominance in the space. To understand how this happened, it’s worth examining the company’s guiding principles.

Eye-roll alert: Every company has principles, missions, visions, values; the vast majority are indistinguishable and sound as if they were written by committees, which they probably were. Some of Amazon’s leadership principles, as they’re called—there are 16—sound that way, until they get a little “peculiar,” to use a favorite Amazonian word.

For example, principle No. 11 begins, “Earn trust.” Leaders, it explains, “are vocally self-critical, even when doing so is awkward or embarrassing. Leaders do not believe their or their team’s body odor smells of perfume.” This peculiarity is a badge of pride at Amazon; its web page for job seekers even says that its use of the principles “is just one of the things that makes Amazon peculiar.”

Not every Amazonian observes every principle all the time; in a company of 1.5 million employees, that’s not realistic. But Amazon’s batting average is high.

To answer the basic question of why a retailer would even think of creating AWS, consider principle No. 1, seemingly the hoariest of them all: “Customer obsession.” Amazon sees itself as a tech company and sees the world as 8 billion potential customers. That’s one reason AWS made sense for a bookseller.

“I cannot tell you the number of times I got asked … ‘But what does this have to do with selling books?’” Adam Selipsky, CEO, Amazon Web Services

Amazon allows new projects lots of time, as with AWS, in part to make sure decisions are based on data. An unusual principle states that leaders “work to disconfirm their beliefs.” Groupthink is comforting, contagious, and dangerous. Being able to invoke one of the principles enables doubters to speak up. 

“We have senior engineers who will stop a meeting and say, ‘We’ve got to disconfirm our beliefs—we’re going too far here without checking,’ ” says Mai-Lan Tomsen Bukovec, who oversees AWS’s storage services. “That’s actually kind of revolutionary in terms of corporate culture.”

It’s not a culture for everyone. Amazon is a famously demanding place to work, and there are plenty of stories of employees who found it to be too much. Media reports have criticized Amazon’s treatment of workers, and the company is battling unionization efforts at some of its e-commerce warehouses. It’s noteworthy that last year Amazon added a new leadership principle: “Strive to be Earth’s best employer.”

“It’s not good for our business and not good for our customers if we turn out great employees and burn them out, and they leave after a couple of years,” says Matt Garman, an early AWS employee who now oversees sales and marketing. “Sometimes there are people who don’t like the culture, don’t like those leadership principles. It’s not a good fit for them. People like the culture or they don’t like the culture, and I think that’s okay. But we want people here for the long term.”

Asked to describe AWS’s strategy, Tomsen Bukovec says, “That’s not a word we use a ton.” 

The foundation of conventional strategy, the subject of hundreds of books and articles, is understanding a company’s industry and competitors. That approach gets us nowhere with Amazon. What industry is it in? No one industry encompasses selling dog food and selling computing power. 

So does Amazon even have a strategy? “Yes,” says Ram Charan, an adviser to CEOs and boards, and coauthor of a book on Amazon’s management system. But “it’s not a competitive strategy,” he says. “It’s a customer strategy.” 

That’s a mind bender. Business is competition, and business strategy is inherently competitive strategy. Except that at Amazon it isn’t. If it had been—if Amazon had been conventionally competitor-focused—AWS probably wouldn’t exist. 

Colin Bryar, a former Amazon executive, says he’s often asked what Amazon is going to build next. Can it repeat what it did with AWS, create an out-of-the-blue business, unexpected and underestimated, in which it becomes dominant? “That’s not the first question Amazon asks,” Bryar says. “They ask, ‘What’s the next big customer problem we can go try to solve?’ ”

The word “big” is key. At Amazon’s size—analysts expect revenue exceeding $500 billion for 2022—small problems are simply not of interest. When company leaders identify a sufficiently big problem, they must then conclude that Amazon can solve it, and that customers will adopt the solution. Those are not easy or quick questions to answer.

Cloud computing will grow 20% annually through 2026, far faster than any other segment of infotech, according to the Gartner tech consulting firm. It’s no longer just smaller companies and startups who don’t want to invest in their own server systems. Many AWS customers are increasing their spend, and some “spend literally hundreds of millions of dollars per year on AWS,” says Gartner analyst Raj Bala, who sees the contracts. “I’m not shocked anymore to see a $200 million annual commitment, which is astonishing.”

Yet AWS’s dominance of the market will likely diminish even as its revenue grows. With a 44% share of the market, AWS has 20 points over Microsoft’s 24%—but that lead is shrinking, says Bala. “In the next five, six, seven years, that gap is going to be very, very narrow, if not equal.” That’s because “a lot of late adopter enterprises are coming to market,” he says, “and a lot of these folks will gravitate to Microsoft because they’ve got an existing contractual relationship with Microsoft.”

The narrowing gap with Microsoft is probably inevitable. AWS’s great challenge for the future is to maintain the discipline that made it a global colossus.

Losing that discipline is insidiously easy. Jim Collins, author of Good to Great , which identifies the factors shared by the world’s most successful companies, has also written an analysis of failure, How the Mighty Fall . Winners invariably maintain discipline, and loss of discipline is always an element of decline. One of the principle threats? Attempts to control workers by overregulating them. “Bureaucracy subverts discipline,” he tells Fortune. 

When a company is growing as fast as AWS, it can be tempting to weaken hiring standards. “As you grow, you start to bring in some of the wrong people,” he says, speaking of companies generally. “If they don’t get the intensity of being there, they shouldn’t be there, but if enough of them stay, you try to control them with bureaucracy. Then the right people get out, which creates a cycle.”

With success and growth come further threats to discipline. When a business is riding high, “easy cash erodes cost discipline, and that discipline is hard to recover once you lose it,” he says. Expansion brings risks, too: Responding desperately to deteriorating performance, the business bets on “undisciplined discontinuous leaps”—acquisitions or expansions for which it isn’t ready. 

At the top of its game, bigger and stronger than any competitor, AWS must now meet an enviable challenge but a challenge nonetheless: the curse of success. Its most crucial task is to maintain the unwavering rigor—the discipline—of its principles and processes.

Selipsky seems to understand the need. Asked to define his job, he is silent for several seconds. Then, quietly but emphatically, he says his job “is to ensure that the positive, productive, useful elements of what got us to this stage—that we hold those dear, and we safeguard them, and we don’t let them slip away. We don’t become incumbents.” 

Amazon’s next big, thorny problem to solve

What might be the next industry to get Amazon’s AWS-style mega-venture treatment? The leading candidate is health care. 

In 2018 Amazon bought PillPack, an online pharmacy, and last summer it paid $3.9 billion for One Medical, a membership-based primary-care provider operating across the U.S., saying in its announcement that “we think health care is high on the list of experiences that need reinvention.” 

No one would disagree. For a company that seeks big problems to solve, this may be the biggest opportunity of all. Health care is the largest sector of the U.S. economy, and the industry is growing fast worldwide. 

Data is the problem at the heart of health care’s inefficiency and unfathomable, wearisome customer experiences—and it’s possible that it could be the solution.

That data is staggering in quantity and mostly unstructured—handwritten notes and X-ray and lab reports, sometimes of life-and-death importance—in an industry that is the last bastion of fax machines. 

It’s a particularly attractive conundrum to Amazon because of the company’s dominance of cloud computing. AWS is already deeply entrenched in the industry, used by hospitals, pharma companies, equipment makers, insurers, pharmacy benefit managers, the Centers for Medicare and Medicaid Services, and more. 

Another potential advantage is Amazon’s massive international workforce and its enormous health care needs and expenses. Just as Amazon developed AWS by observing its own software needs and seeing them mirrored elsewhere, its own challenges as a growing corporate behemoth now may point the way to a new market opportunity.

This article appears in the December 2022/January 2023 issue of Fortune with the headline, “How Amazon’s cloud took the world by storm.”

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Cite this post.

AMS Citation

Cois, C., 2015: DevOps Case Study: Amazon AWS. Carnegie Mellon University, Software Engineering Institute's Insights (blog), Accessed March 30, 2024, https://insights.sei.cmu.edu/blog/devops-case-study-amazon-aws/.

APA Citation

Cois, C. (2015, February 5). DevOps Case Study: Amazon AWS. Retrieved March 30, 2024, from https://insights.sei.cmu.edu/blog/devops-case-study-amazon-aws/.

Chicago Citation

Cois, C. Aaron. "DevOps Case Study: Amazon AWS." Carnegie Mellon University, Software Engineering Institute's Insights (blog) . Carnegie Mellon's Software Engineering Institute, February 5, 2015. https://insights.sei.cmu.edu/blog/devops-case-study-amazon-aws/.

IEEE Citation

C. Cois, "DevOps Case Study: Amazon AWS," Carnegie Mellon University, Software Engineering Institute's Insights (blog) . Carnegie Mellon's Software Engineering Institute, 5-Feb-2015 [Online]. Available: https://insights.sei.cmu.edu/blog/devops-case-study-amazon-aws/. [Accessed: 30-Mar-2024].

BibTeX Code

@misc{cois_2015, author={Cois, C. Aaron}, title={DevOps Case Study: Amazon AWS}, month={Feb}, year={2015}, howpublished={Carnegie Mellon University, Software Engineering Institute's Insights (blog)}, url={https://insights.sei.cmu.edu/blog/devops-case-study-amazon-aws/}, note={Accessed: 2024-Mar-30} }

DevOps Case Study: Amazon AWS

C. Aaron Cois

February 5, 2015, published in.

Regular readers of this blog will recognize a recurring theme in this series: DevOps is fundamentally about reinforcing desired quality attributes through carefully constructed organizational process, communication, and workflow . When teaching software engineering to graduate students in Carnegie Mellon University's Heinz College , I often spend time discussing well known tech companies and their techniques for managing software engineering and sustainment. These discussions serve as valuable real-world examples for software engineering approaches and associated outcomes, and can serve as excellent case studies for DevOps practitioners. This posting will discuss one of my favorite real-world DevOps case studies: Amazon .

Amazon is one of the most prolific tech companies today. Amazon transformed itself in 2006 from an online retailer to a tech giant and pioneer in the cloud space with the release of Amazon Web Services (AWS) , a widely used on-demand Infrastructure as a Service (IaaS) offering. Amazon accepted a lot of risk with AWS. By developing one of the first massive public cloud services, they accepted that many of the challenges would be unknown, and many of the solutions unproven. To learn from Amazon's success we need to ask the right questions. What steps did Amazon take to minimize this inherently risky venture? How did Amazon engineers define their process to ensure quality?

Luckily, some insight into these questions was made available when Google engineer Steve Yegge (a former Amazon engineer) accidentally made public an internal memo outlining his impression of Google's failings (and Amazon's successes) at platform engineering. This memo (which Yegge has specifically allowed to remain online) outlines a specific decision that illustrates CEO Jeff Bezos's understanding of the underlying tenets of what we now call DevOps, as well as his dedication to what I will claim are the primary quality attributes of the AWS platform: interoperability, availability, reliability, and security. According to Yegge, Jeff Bezos issued a mandate during the early development of the AWS platform, that stated, in Yegge's words :

• All teams will henceforth expose their data and functionality through service interfaces.
• Teams must communicate with each other through these interfaces.
• There will be no other form of interprocess communication allowed: no direct linking, no direct reads of another team's data store, no shared-memory model, no back-doors whatsoever. The only communication allowed is via service interface calls over the network.
• It doesn't matter what technology they use. HTTP, Corba, Pubsub, custom protocols -- doesn't matter. Bezos doesn't care.
• All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions.
• Anyone who doesn't do this will be fired.

Aside from the harsh presentation, take note of what is being done here. Engineering processes are being changed; that is, engineers at Amazon now must develop web service APIs to share all data internally across the entire organization. This change is specifically designed to incentivize engineers to build for the desired level of quality. Teams will be required to build usable APIs, or they will receive complaints from other teams needing to access their data. Availability and reliability will be enforced in the same fashion. As more completely unrelated teams need to share data, APIs will be secured as a means of protecting data, reducing resource usage, auditing, and restricting access from untrusted internal clients. Keep in mind that this mandate was to all teams, not just development teams. Marketing wants some data you have collected on user statistics from the web site? Then marketing has to find a developer and use your API. You can quickly see how this created a wide array of users, use cases, user types, and scenarios of use for every team exposing any data within Amazon.

DevOps teaches us to create a process that enforces our desired quality attributes, such as requiring automated deployment of our software to succeed before the continuous integration build can be considered successful. In effect, this scenario from Amazon is an authoritarian version of DevOps thinking. By enforcing a rigorous requirement of eating (and serving!) their own dog food to all teams within Amazon, Bezos's engineering operation ensures that through constant and rigorous use, their APIs would become mature, robust, and hardened.

These API improvements happened organically at Amazon, without the need to issue micromanaging commands such as "All APIs within Amazon must introduce rate limit X and scale to Y concurrent requests," because teams were incentivized to continually improve their APIs to make their own working lives easier. When AWS was released a few years later, many of these same APIs comprised the public interface of the AWS platform, which was remarkably comprehensive and stable at release. This level of quality at release directly served business goals by contributing to the early adoption rates and steady increase in popularity of AWS, a platform that provided users with a comprehensive suite of powerful capabilities and immediate comfort and confidence in a stable, mature service.

Every two weeks, the SEI will publish a new blog post offering guidelines and practical advice to organizations seeking to adopt DevOps in practice. We welcome your feedback on this series, as well as suggestions for future content. Please leave feedback in the comments section below.

Additional Resources

To listen to the podcast, DevOps--Transform Development and Operations for Fast, Secure Deployments , featuring Gene Kim and Julia Allen, please visit https://resources.sei.cmu.edu/library/asset-view.cfm?assetid=58525 .

Author Page

Digital library publications, send a message, more by the author, devops case study: netflix and the chaos monkey, april 30, 2015 • by c. aaron cois, continuous integration in devops, april 8, 2015 • by c. aaron cois, january 26, 2015 • by c. aaron cois, devops and your organization: where to begin, december 18, 2014 • by c. aaron cois, devops and agile, november 13, 2014 • by c. aaron cois, more in devsecops, example case: using devsecops to redefine minimum viable product, march 11, 2024 • by joe yankel, acquisition archetypes seen in the wild, devsecops edition: clinging to the old ways, december 18, 2023 • by william e. novak, extending agile and devsecops to improve efforts tangential to software product development, august 7, 2023 • by david sweeney , lyndsi a. hughes, 5 challenges to implementing devsecops and how to overcome them, june 12, 2023 • by joe yankel , hasan yasar, actionable data from the devsecops pipeline, may 1, 2023 • by bill nichols , julie b. cohen, get updates on our latest work..

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Exploring AWS OpenSearch: A guide to use cases, features, and deployment

What is aws opensearch.

AWS OpenSearch is a powerful search and analytics service, based on Elasticsearch and made available in the AWS cloud. It’s designed to provide scalable and performant search capabilities for a broad range of applications and use cases. With OpenSearch, developers can efficiently index and search huge amounts of structured and unstructured data in real-time, enabling users to find the information they need quickly and accurately. OpenSearch then integrates with other AWS services, allowing you to design comprehensive cloud based search solutions. Example use cases for AWS OpenSearch include search functionality for websites, log data analysis, or personalized recommendations.

OpenSearch Service vs. OpenSearch Serverless

There are two primary options for running OpenSearch on AWS: the OpenSearch (managed) Service and OpenSearch Serverless Service . These two deployment options have different use cases and features, and it’s important to understand the differences between them before deciding which one is right for your application. At superluminar we have experience with both deployment options and despite our enthusiasm for serverless solutions we have had cases in which the managed service was the better choice. We’ll outline some of the differences below.

But first, a note on terminology. In the context of the AWS OpenSearch managed service, an OpenSearch domain refers to your OpenSearch cluster and the data stored within it. The domain has a collection of nodes that work together to provide search and analytics functionality. A node is a single instance of OpenSearch.

An OpenSearch Serverless collection is a group of OpenSearch indexes that work together to support a specific workload or use case. OpenSearch collections are deployed in a serverless cluster. OpenSearch Serverless offers 3 types of collection: timeseries, search, and a new vector search option to support semantic search use cases. The type of collection you choose determines the features available to you and optimizes the underlying cluster for your workload type.

So, when we refer to a OpenSearch serverless workload, we will talk in terms of collections and when we are talking about the OpenSearch managed service we will talk in terms of domains.

Scalability and Performance:

The first and most obvious difference between the OpenSearch managed service and the OpenSearch serverless service is how they handle scaling and performance.

The OpenSearch managed service offers tighter control over scalability and performance options than the serverless variant. You can choose the number of nodes, their specifications, and scale up or down as needed to accommodate varying workloads. Importantly, though, this is something you have to manage yourself. You have control over the configuration but you may need to invest considerable time and effort in the management of the domain and its resources. This is the typical trade off between control and convenience that we see in many AWS services that offer both a managed and serverless deployment option.

The OpenSearch serverless service meanwhile abstracts away most management complexities. It automatically scales based on demand, handling spikes and fluctuations in traffic without manual intervention. You select the capacity of your service in so called OpenSearch Capacity Units (OCUs). There are two types of OCU, search and indexing capacity units and on deployment of a collection, you select the number of each type of OCU. By default you get 1 search OCU and 1 indexing OCU in 2 availability zones, so a total of 4 OCUs are deployed. This means you are charged for 4 OCUs (I’ll talk more about costs later). These units are a combination of 6 GiB of memory and corresponding virtual CPU (vCPU) and data transfer to storage in S3.

The service automatically scales up or down based on the number of capacity units you select. This is a great option for smaller workloads or startups as it allows you to focus on building your application’s search functionality without worrying about cluster provisioning, scaling, or patching. As new versions are released, OpenSearch Serverless will automatically upgrade your collections to include new features, bug fixes, and performance improvements.

Customisation:

Customisability is also worth keeping in mind. The OpenSearch managed service allows for extensive customisation. You can configure advanced index settings, add plugins, and fine-tune the domain to match your application’s specific requirements. Plugins are a powerful feature of OpenSearch and can be used to add functionality to support your workload. For example, you can add plugins to support machine learning, security, or monitoring.

The availability of plugins for the OpenSearch serverless service is improving rapidly but if you are looking to migrate an OpenSearch cluster to AWS, and rely heavily on plugins, you should definitely check their availability ahead of time. In general the serverless service currently offers less customisation compared to the OpenSearch managed service. While you can configure indices and mappings, some advanced settings might not be available. However, if you are not heavily reliant on customisable features, this should not be a problem. There are still a lot of built-in features that are available out of the box.

Cost Structure:

The cost structure for the OpenSearch managed service is based on the type and number of instances you provision, along with storage and data transfer fees. You are billed based on the number and type of instances you provision for your domain and costs increase with higher-performing instance types. If you have a very steady workload and plan to use the service for a long period of time you can still reduce these costs by reserving OpenSearch instances AWS offers reserved instance pricing for longer-term commitments, which can provide cost savings over on-demand pricing for a one or three year period. You then pay for storage according to the EBS volume type you choose and for data transfer to and from your domain. There are options to reduce the cost of your data storage as well. For example you can place rarely or less searched data into so-called ‘ultrawarm’ as opposed to ‘hot’ nodes. In this case an ultrawarm node refers to data stored in S3 as opposed to hot node data that is stored on instance or EBS volumes, which are more rapidly accessible. You can find more information on the pricing structure here .

We have already introduced the idea of OCUs for the OpenSearch serverless service and the fact that you always have four running by default. At the time of writing, it is not possible to scale down to zero OCUs. This means that even if you have no traffic at all for a period of time, you will still be charged for 4 OCUs. Currently in the Frankfurt region indexing and search OCUs each cost $0.339 per hour. This means that if you have no traffic for a month, you will still be charged around $900 + storage costs of $0.026 per GB per month in S3. This might not sound great, but depending on the capacity you might require at peak usage of your service, it could still work out cheaper than having a large cluster without adequate capacity scaling, alongside maintenance effort costs. The serverless service additionally handles caching for you, if dealing with timeseries data. Frequently accessed data are stored in a ‘hot’ cache to optimise request response times. The OpenSearch managed service requires you to handle data lifecycles yourself. When we are working with customers, we typically use the AWS pricing calculator to estimate the costs of various options and compare them to the expected usage of the service. This is a good way to determine which service is best for your use case.

For more general info on controlling AWS costs, check out our blog article on the topic.

Our anecdotal experience with AWS OpenSearch deployment options

We recently built sample applications in CDK using both the OpenSearch managed service and OpenSearch serverless service to get a general idea of how the configuration and ease of use differ. The applications allowed us to search for movies by title and genre via an API gateway, a lambda and opensearch. We used the movieId as the document id and the title and genres as the searchable fields. It’s a common example used in several tutorials (for example, here ) and we found it a good hands on approach to explore the configuration possibilities.

Documentation

The AWS OpenSearch managed Service is a mature product with a large community of users and contributors and its history as an Elasticsearch fork means that there is a lot of existing documentation and guides available.

The serverless option is relatively new and the documentation is undergoing a lot of changes at the time of writing. This can sometimes result in some difficulties working with the service due to mistakes or incompletely documented features. Inevitably there are then also fewer guides available but there are enough to get started with.

In terms of disaster recovery for production search solutions, it’s worth noting that cross cluster replication i.e. replicating indexes, mappings, and metadata from one OpenSearch domain to another, is not currently available for serverless OpenSearch. There is also no snapshot functionality to create backups at present, but automating the backup of your data to S3 is one solution. If the requirement is high availability though, the serverless variant of OpenSearch is multi-AZ per default, a search and index OCU in one AZ and the same in a second AZ. You can take a look here for current feature limitation of the OpenSearch serverless service. Despite these limitations, I would expect to see cross cluster replication and several other features of the OpenSearch managed service rolled out to the OpenSearch serverless service in the future.

Ease of use

Even with occasionally shoddy documentation, configuration of the serverless service was straightforward. Unsurprisingly the managed service was also easy to set up but required more configuration and was more time consuming as a result.

Both the AWS OpenSearch managed service and the AWS OpenSearch serverless service offer powerful search and analytics capabilities. Before jumping into using one or the other, you will need a clear idea of the features that are necessary for your application and your expected workload. If you need help to work out what kind of setup is best for your use case, or if you need help with the implementation, we are happy to help. Get in contact with us .

Rebecca is a Cloud Consultant at superluminar . After a career change from neuropsychology, she works in IT and is passionate about automation and data engineering. You can find her here on LinkedIn .

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AWS Case Studies: Services and Benefits in 2024

Home Blog Cloud Computing AWS Case Studies: Services and Benefits in 2024

With its extensive range of cloud services, Amazon Web Services (AWS) has completely changed the way businesses run. Organisations demonstrate how AWS has revolutionized their operations by enabling scalability, cost-efficiency, and innovation through many case studies. AWS's computing power, storage, database management , and artificial intelligence technologies have benefited businesses of all sizes, from startups to multinational corporations. These include improved security, agility, worldwide reach, and lower infrastructure costs. With Amazon AWS educate program it helps businesses in various industries to increase growth, enhance workflow, and maintain their competitiveness in today's ever-changing digital landscape. So, let's discuss the AWS cloud migration case study   and its importance in getting a better understanding of the topic in detail.

What are AWS Case Studies, and Why are They Important?

The   AWS case   studies comprehensively explain how companies or organizations have used Amazon Web Services (AWS) to solve problems, boost productivity, and accomplish objectives. These studies provide real-life scenarios of Amazon Web Services (AWS) in operation, showcasing the wide range of sectors and use cases in which AWS can be successfully implemented. They offer vital lessons and inspiration for anyone considering or already using AWS by providing insights into the tactics, solutions, and best practices businesses use the AWS Cloud Engineer program . The Amazon ec2 case study   is crucial since it provides S's capabilities, assisting prospective clients in comprehending the valuable advantages and showcasing AWS's dependability, scalability, and affordability in fostering corporate innovation and expansion.

What are the Services Provided by AWS, and What are its Use Cases?

The   case study on AWS in Cloud Computing provided and its use cases mentioned:

Elastic Compute Cloud (EC2) Use Cases

Amazon Elastic Compute Cloud (EC2) enables you to quickly spin up virtual computers with no initial expenditure and no need for a significant hardware investment. Use the AWS admin console or automation scripts to provision new servers for testing and production environments promptly and shut them down when not in use.

AWS EC2 use cases consist of:

• With options for load balancing and auto-scaling, create a fault-tolerant architecture.
• Select EC2 accelerated computing instances if you require a lot of processing power and GPU capability for deep learning and machine learning.

Relational Database Service (RDS) Use Cases

Since Amazon Relational Database Service (Amazon RDS) is a managed database service, it alleviates the stress associated with maintaining, administering, and other database-related responsibilities.

AWS RDS uses common cases, including:

• Without additional overhead or staff expenditures, a new database server can be deployed in minutes and significantly elevate dependability and uptime. It is the perfect fit for complex daily database requirements that are OLTP/transactional.
• RDS should be utilized with NoSQL databases like Amazon OpenSearch Service (for text and unstructured data) and DynamoDB (for low-latency/high-traffic use cases).

AWS Workspaces

AWS offers Amazon Workspaces, a fully managed, persistent desktop virtualization service, to help remote workers and give businesses access to virtual desktops within the cloud. With it, users can access the data, apps, and resources they require from any supported device, anywhere, at any time.

AWS workspaces use cases

• IT can set up and manage access fast. With the web filter, you can allow outgoing traffic from a Workspace to reach your chosen internal sites.
• Some companies can work without physical offices and rely solely on SaaS apps. Thus, there is no on-premises infrastructure. They use cloud-based desktops via AWS Workspaces and other services in these situations.

AWS Case Studies

Now, we'll be discussing different case studies of AWS, which are mentioned below: -

Case Study - 1: Modern Web Application Platform with AWS

American Public Media, the programming section of Minnesota Public Radio, is one of the world's biggest producers and distributors of public television. To host their podcast, streaming music, and news websites on AWS, they worked to develop a proof of concept.

After reviewing an outdated active-passive disaster recovery plan, MPR decided to upgrade to a cloud infrastructure to modernize its apps and methodology. This infrastructure would need to be adaptable to changes within the technology powering their apps, scalable to accommodate their audience growth, and resilient to support their disaster recovery strategy.

MPR and AWS determined that MPR News and the public podcast websites should be hosted on the new infrastructure to show off AWS as a feasible choice. Furthermore, AWS must host multiple administrative apps to demonstrate its private cloud capabilities. These applications would be an image manager, a schedule editor, and a configuration manager.

To do this, AWS helped MPR set up an EKS Kubernetes cluster . The apps would be able to grow automatically according to workload and traffic due to the cluster. AWS and MPR developed Elasticsearch at Elastic.co and a MySQL instance in RDS to hold application data.

Business Benefits

Considerable cost savings were made possible by the upgraded infrastructure. Fewer servers would need to be acquired for these vital applications due to the decrease in hardware requirements. Additionally, switching to AWS made switching from Akamai CDN to CloudFront simple. This action reduced MPR's yearly expenses by thousands.

Case Study - 2: Platform Modernisation to Deploy to AWS

Foodsby was able to proceed with its expansion goals after receiving a $6 million investment in 2017, but it still needed to modernize its mobile and web applications. For a faster time to launch to AWS, they improved and enhanced their web, iOS, and Android applications.

Sunsetting technology put this project on a surged timeline. Selecting the mobile application platform required serious analysis and expert advice to establish consensus across internal stakeholders.

Improving the creation of front-end and back-end web apps that separated them into microservices to enable AWS hosting, maximizing scalability. Strengthening recommended full Native for iOS and Android and quickly creating and implementing that solution.

Case Study - 3: Cloud Platform with Kubernetes

SPS Commerce hired AWS to assist them with developing a more secure cloud platform, expanding their cloud deployment choices through Kubernetes, and educating their engineers on these advanced technologies.

SPS serves over 90,000 retail, distribution, grocery, and e-commerce businesses. However, to maintain its growth, SPS needs to remove obstacles to deploying new applications on AWS and other cloud providers in the future. They wanted a partner to teach their internal development team DevOps principles and reveal them to Kubernetes best practices, even though they knew Kubernetes would help them achieve this.

To speed up new project cycle times, decrease ramp-up times, and improve the team's Kubernetes proficiency, it assisted with developing a multi-team, Kubernetes-based platform with a uniform development method. The standards for development and deployment and assisted them in establishing the deployment pipeline.

Most teams can plug, play, and get code up and running quickly due to the streamlined deployment interface. SPS Commerce benefits from Kubernetes' flexibility and can avoid vendor lock-in, which they require to switch cloud providers.

Case Study - 4: Using Unified Payment Solutions to Simplify Government Services

The customer, who had a portfolio of firms within its authority, needed to improve experience to overcome the difficulty of combining many payment methods into a single, unified solution.

Due to the customers' varied acquisitions, the payment system landscape became fragmented, making it more difficult for clients to make payments throughout a range of platforms as well as technologies. Providing a streamlined payment experience could have been improved by this lack of coherence and standardization.

It started developing a single, cloud-based payment system that complies with the customers' microservices-based reference design. CRUD services were created after the user interface for client administration was set at the beginning of the project.

With this, the customer can streamline operations and increase efficiency by providing a smooth payment experience.

The new system demonstrated a tremendous improvement over the old capability, demonstrating the ability to handle thousands of transactions per second.

Maintaining system consistency and facilitating scalability and maintenance were made more accessible by aligning with the reference architecture.

Case Study - 5: Accelerated Data Migration to AWS

They selected improvements to create   an   AWS cloud migration case study cloud platform to safely transfer their data from a managed service provider to AWS during the early phases of a worldwide pandemic.

Early in 2020, COVID-19 was discovered, and telemedicine services were used to lessen the strain on hospital infrastructure. The number of telehealth web queries increased dramatically overnight, from 5,000 to 40,000 per minute. Through improvement, Zipnosis was able to change direction and reduce the duration of its AWS migration plan from six to three months. The AWS architecture case study includes HIPAA, SOC2, and HITRUST certification requirements. They also wanted to move their historic database smoothly across several web-facing applications while adhering to service level agreements (SLAs), which limited downtime.

Using Terraform and Elastic Kubernetes Service, the AWS platform creates a modern, infrastructure-as-code, HIPAA-compliant, and HITRUST-certified environment. With the help of serverless components, tools were developed to roll out an Application Envelope, enabling the creation of a HIPAA-compliant environment that could be activated quickly.

Currently, Zipnosis has internal platform management. Now that there is more flexibility, scaling up and down is more affordable and accessible. Their services are more marketable to potential clients because of their scalable, secure, and efficient infrastructure. Their use of modern technologies, such as Kubernetes on Amazon EKS, simplifies hiring top people. Zipnosis is in an excellent position to move forward.

Case Study - 6: Transforming Healthcare Staffing

The customer's outdated application presented difficulties. It was based on the outdated DBROCKET platform and needed an intuitive user interface, testing tools , and extensibility. Modernizing the application was improving the job and giving the customer an improved, scalable, and maintainable solution.

Although the customer's old application was crucial for predicting hospital staffing needs, maintenance, and improvements were challenging due to its reliance on the obscure DBROCKET platform. Hospitals lost money on inefficient staff scheduling due to the application's lack of responsiveness and a mobile-friendly interface.

Choosing Spring Boot and Groovy for back-end development to offer better maintainability and extensibility throughout the improved migration of the application from DBROCKET to a new technology stack. Unit tests were used to increase the reliability and standard of the code.

Efficiency at Catalis increased dramatically when the advanced document redaction technology was put in place. They were able to process papers at a significantly higher rate because the automated procedure cut down the time and effort needed for manual redaction.

Catalis cut infrastructure costs by utilizing serverless architecture and cloud-based services. They saved a significant amount of money because they were no longer required to upgrade and maintain on-premises servers.

The top-notch Knowledgehut best Cloud Computing courses that meet different demands and skill levels are available at KnowledgeHut. Through comprehensive curriculum, hands-on exercises, and expert-led instruction, attendees may learn about and gain practical experience with cloud platforms, including AWS, Azure, Google Cloud, and more. Professionals who complete these courses will be efficient to succeed in the quickly developing sector of cloud computing.

Finally,   a   case study of   AWS retail case studies offers a range of features and advantages. These studies show how firms in various industries use AWS for innovation and growth, from scalability to cost efficiency. AWS offers a robust infrastructure and a range of technologies to satisfy changing business needs, whether related to improving customer experiences with cloud-based solutions or streamlining processes using AI and machine learning. These case studies provide substantial proof of AWS's influence on digital transformation and the success of organizations.

Frequently Asked Questions (FAQs)

From the case study of Amazon web services, companies can learn how other businesses use AWS services to solve real-world problems, increase productivity, cut expenses, and innovate. For those looking to optimize their cloud strategy and operations, these case studies provide insightful information, optimal methodologies, and purpose. 

You can obtain case studies on AWS through the AWS website, which has a special section with a large selection of case studies from different industries. In addition, AWS releases updated case studies regularly via various marketing platforms and on its blog.

The case study of Amazon web services, which offers specific instances of how AWS services have been successfully applied in various settings, can significantly assist in the decision-making process for IT initiatives. Project planning and strategy can be informed by the insights, best practices, and possible solutions these case studies provide.

Kingson Jebaraj

Kingson Jebaraj is a highly respected technology professional, recognized as both a Microsoft Most Valuable Professional (MVP) and an Alibaba Most Valuable Professional. With a wealth of experience in cloud computing, Kingson has collaborated with renowned companies like Microsoft, Reliance Telco, Novartis, Pacific Controls UAE, Alibaba Cloud, and G42 UAE. He specializes in architecting innovative solutions using emerging technologies, including cloud and edge computing, digital transformation, IoT, and programming languages like C, C++, Python, and NLP. 

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Case Studies

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In today’s dynamic business landscape, organizations are constantly seeking ways to enhance their agility, optimize operations, and drive innovation. Embracing the power of cloud computing has become a strategic imperative for many, unlocking a world of scalability, flexibility, and cost-effectiveness. However, navigating the complexities of cloud adoption and management can be daunting, especially for organizations without in-house expertise.

Learn how public cloud managed services , professional services , and cloud transformation can help your organization. You can read more AWS case studies here .

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Research: How Different Fields Are Using GenAI to Redefine Roles

• Maryam Alavi

Examples from customer support, management consulting, professional writing, legal analysis, and software and technology.

The interactive, conversational, analytical, and generative features of GenAI offer support for creativity, problem-solving, and processing and digestion of large bodies of information. Therefore, these features can act as cognitive resources for knowledge workers. Moreover, the capabilities of GenAI can mitigate various hindrances to effective performance that knowledge workers may encounter in their jobs, including time pressure, gaps in knowledge and skills, and negative feelings (such as boredom stemming from repetitive tasks or frustration arising from interactions with dissatisfied customers). Empirical research and field observations have already begun to reveal the value of GenAI capabilities and their potential for job crafting.

There is an expectation that implementing new and emerging Generative AI (GenAI) tools enhances the effectiveness and competitiveness of organizations. This belief is evidenced by current and planned investments in GenAI tools, especially by firms in knowledge-intensive industries such as finance, healthcare, and entertainment, among others. According to forecasts, enterprise spending on GenAI will increase by two-fold in 2024 and grow to $151.1 billion by 2027 .

• Maryam Alavi is the Elizabeth D. & Thomas M. Holder Chair & Professor of IT Management, Scheller College of Business, Georgia Institute of Technology .

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IBM Cloud Case Study Entry – American Airlines

Cloud computing has become essential for businesses seeking operational agility and efficiency. I selected the American Airlines case from the IBM Could Case Study. I will explain some of the advantages and disadvantages of cloud computing. The world’s largest passenger airline, American Airlines, founded in 1930, announced on June 28 that it has selected the IBM Cloud. (Big Blue has announced the discontinuation of the BlueMix Cloud brand.)

The world’s largest passenger airline, founded in 1930, announced on June 28 that it has selected the IBM Cloud. (Big Blue has announced that they are discontinuing the BlueMix Cloud brand.) I chose this case study because I work for airlines and have seen their history with new technology platforms and the latest developments. Simplifies business operations and enables operations with fewer personnel. However, it may become difficult to hear customer voices, and the quality of customer service may decline due to a decline in service.

Advantage:　　　　　　　　　　　　　　　　　　　　　　　　　　　 Lower costs: Cloud computing reduces the need for initial capital expenditures on hardware and infrastructure1. Strategic edge: Access to the latest technology and applications gives you a competitive advantage. Fast: Rapid service deployment allows companies to bring products to market quickly. Backup and restore: Backing up and restoring data is usually more efficient in the cloud. Reliability: Cloud services often provide high reliability because updates and changes are communicated instantly1. Mobility: Employees can access cloud services from anywhere with an internet connection. Unlimited storage capacity : Cloud services typically offer scalable storage options. Collaboration: Cloud platforms facilitate collaboration between geographically dispersed teams

Cons:　 　　　　　　　　　　　　　　　　　　　　　　　　　　　Downtime: Cloud services are subject to outages that can impact access to your data and applications. Security concerns: Storing sensitive data offsite can raise security and privacy concerns. Less control: Businesses may have less control over the management of their data and services2. Vendor lock-in: Switching cloud providers can be difficult and can lead to dependence on a single provider.　When it comes to IBM cloud solutions for American Airlines, it appears to be a strategic move to improve customer experience and operational efficiency. Migrating to IBM Cloud reportedly lowers costs, improves operational reliability, and speeds the development and release of new applications3.

If I worked for American Airlines, I would buy into the IBM Cloud solution because of my past airline experience. It will depend on a variety of factors, including your specific business needs, compatibility of IBM Cloud with your company’s existing systems, and the potential risks involved. However, based on the reported results, the IBM Cloud solution is consistent with American Airlines’ goal of transforming the customer experience through technology. Before making such a decision, it is important to conduct a thorough analysis and consider both the advantages and potential disadvantages. We believe that the right cloud solution must be aligned with a company’s long-term strategy and operational goals.

Preimesberger, C. (2021, February 2). American Airlines Heads for a New Cloud with IBM . eWEEK. https://www.eweek.com/cloud/american-airlines-heads-for-a-new-cloud-with-ibm/

Advantages of Cloud Computing | Google Cloud . (n.d.). Google Cloud. https://cloud.google.com/learn/advantages-of-cloud-computing

American Airlines’ two new app features . (n.d.). TAG Group. https://www.tag-group.com/group/news/american-airlines-two-new-app-features

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Homeschooling Experiences of Kenyan Mothers of Children with Dyslexia during the Covid-19 Pandemic

A multiple case study.

• ojsadmin ojsadmin
• June Jane Ombara University of Nairobi http://orcid.org/0009-0009-4052-6676
• Hellen N. Inyega University of Nairobi https://orcid.org/0000-0003-3311-6838
• Humphrey Jeremiah Ojwang University of Nairobi http://orcid.org/0009-0003-3782-7095

Previous studies have provided homeschooling experiences of parents, but little attention has been paid to African mothers’ homeschooling their children. Mothers of children with dyslexia spend more time nurturing, socializing, and in care work as compared to non-homeschooling mothers. This study uses a qualitative method from an African Feminist Epistemology lens to explore the lived experiences of homeschooling African mothers. A small case study sample is used to provide a rich, detailed understanding of the phenomena (Scribner and Crow, 2012). Out of ten mothers interviewed from a Nairobi-based social support group for children with dyslexia, two were fully homeschooled during and after the COVID-19 pandemic. The study specifically focuses on the unique experiences of the two homeschooling mothers to provide an African homeschooling context and experience. A narratological method is used to compare and contrast the mothers’ exclusive experiences. Five main themes emerged from the study: strategies used by mothers; impact on careers; mental health; financial independence, and impact on social life. We conclude that mothers' homeschooling mainly resulted from the COVID-19 pandemic. Mothers used wide-ranging creative strategies for successful homeschooling alongside household chores and care work for the family. Homeschooling, however, had a negative impact on mothers’ careers, mental health, financial independence, and social life. We conclude that the lived experiences of homeschooling Kenyan mothers of children with dyslexia may be considered an impression of what other homeschooling Kenyan mothers of children with learning disabilities face.

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