{"id":19057,"date":"2022-11-26T15:07:22","date_gmt":"2022-11-26T15:07:22","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2022\/11\/26\/reinvent-2022-marks-the-next-chapter-in-data-and-cloud\/"},"modified":"2022-11-26T15:07:22","modified_gmt":"2022-11-26T15:07:22","slug":"reinvent-2022-marks-the-next-chapter-in-data-and-cloud","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2022\/11\/26\/reinvent-2022-marks-the-next-chapter-in-data-and-cloud\/","title":{"rendered":"Re:Invent 2022 marks the next chapter in data and cloud"},"content":{"rendered":"

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The ascendancy of Amazon Web Services Inc. under the leadership of Andy Jassy was marked by a tsunami of data and corresponding cloud services to leverage data.\u00a0Those services mainly came in the form of primitives \u2013 basic building blocks that were used by developers to create more sophisticated capabilities.<\/p>\n

AWS in the 2020s, led by Chief Executive Adam Selipsky, will be marked by four high-level trends in our view: 1) a rush of data that will dwarf anything previously seen; 2) a doubling or tripling down on the basic elements of cloud \u2013 compute, storage, database, security and the like; 3) a greater emphasis on end-to-end integration of AWS services to simplify and accelerate customer adoption of cloud; and 4) significantly deeper business integration of cloud, beyond information technology, as an underlying element of organizational operations.<\/p>\n

In this Breaking Analysis, we extract and analyze nuggets from John Furrier\u2019s annual sitdown<\/a> with the CEO of AWS. We\u2019ll share data from Enterprise Technology Research and other sources to set the context for the market and competition in cloud, and we\u2019ll give you our glimpse of what to expect at re:Invent 2022.<\/p>\n

First, a quick update on the hyperscale revenue forecast<\/h3>\n

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Before we get into the core of our analysis, Alibaba this past week\u00a0announced earnings<\/a>\u00a0and we\u2019ve updated our third-quarter hyperscale computing forecast for the\u00a0year, as seen above.<\/p>\n

We won\u2019t spend much time on this, but suffice to say Alibaba\u2019s cloud business is suffering from the macro trend but is seeing a more substantial slowdown than its peers. China headwinds combined with a restructuring of its cloud business has led to significantly slower growth for Alibaba. This puts our year-end estimates for 2022 revenue at $161 billion for all four hyperscalers combined. This still represents a healthy 34% growth, with AWS expected to surpass $80 billion in revenue.<\/p>\n

Optimizing cloud spend is a durable trend<\/h3>\n

On a related note, one of the big themes in cloud that we\u2019ve been reporting is how customers are reassessing their cloud spend. Below is\u00a0a graphic we pulled from AWS\u2019 website that shows the various pricing plans at a high level. As you know, they get much more granular.<\/p>\n

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Basically there are four levels AWS offers. On-demand \u2013 i.e., pay by the drink; spot instances \u2013 i.e., right place, right time I can use that extra capacity; reserved instances, or RIs, where I pay up front to get a discount; and optimized savings plans where customers commit to a one- or three-year term.<\/p>\n

You\u2019ll notice that we labeled the choices in a different order than AWS presents on its website and that\u2019s because we believe the natural progression for customers is to start out on-demand, maybe experiment with spot instances, move to RIs when the cloud bill becomes too onerous and, if you\u2019re large enough, lock in for one or three years.<\/p>\n

We believe on-demand accounts for the majority of AWS customer spending.\u00a0But those on-demand customers are also at-risk customers. Sur, there are switching costs such as egress and learning curve, but many customers have multiple clouds and if you\u2019re not married to AWS with a longer-term commitment, there\u2019s less friction to switch.<\/p>\n

AWS presents the most attractive plan from a financial perspective second after on-demand and it\u2019s also the plan that requires the greatest commitment.<\/p>\n

The trend toward subscription pricing models varies by sector<\/h3>\n

In fairness to AWS, it\u2019s true that there\u2019s a trend toward subscription-based pricing. But it\u2019s less pronounced in infrastructure as a service than software as a service.\u00a0This chart below is from an ETR drill-down survey (N=300). Pay attention to the bars on the right. The pink is subscription-based and the light blue is consumption-based. And you can see there\u2019s a steady trend toward subscription.<\/p>\n

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We\u2019ll dig into this in a later Breaking Analysis episode, but we\u2019ll share now that when it comes to IaaS and platform as a service, 44% of customers either prefer or are required to use on-demand pricing whereas around 40% of customers say they either prefer or are required to use subscription pricing. The further you move up the stack, the more prominent subscription pricing becomes \u2014 often with 60% or more for software-based offerings requiring or preferring subscription. Notably cybersecurity tracks software likely because, as with software, you\u2019re not shutting down your cyber protection on demand.<\/p>\n

Putting data at the core of the business<\/h3>\n

As a preview to our expectations for re:Invent, we\u2019ll make an an observation related to data.<\/p>\n

In his 2018 book Seeing Digital, author\u00a0David Moschella<\/span><\/a>\u00a0made the point that, whereas most companies organize data operations on the periphery of their business \u2013 almost as an add-on function \u2013 mega data companies such as Google LLC, Amazon.com Inc. and Facebook owner Meta Platforms Inc. have placed data at the core of their operations and apply machine intelligence to that foundation.<\/p>\n

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Why is this? The fact is it\u2019s not easy to do what the internet giants have done.<\/p>\n

Embedding machine learning and AI into apps and data is a long-term trend<\/h3>\n

This brings us to re:Invent 2022 and the future of cloud.<\/p>\n

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Machine learning and AI will increasingly be infused into applications. The data stack and applications stack are coming together as organizations build data apps and data products. Data expertise is moving from the domain of highly specialized individuals to everyday business people and we are just at the cusp of this trend.<\/p>\n<\/blockquote>\n

We believe this will be a massive theme of not only re:Invent \u201822 but of cloud in the 2020s. The\u00a0vision of data mesh<\/a>\u00a0will be realized this decade in our view.<\/p>\n

Analyzing nuggets from Selipsky<\/h3>\n

Let\u2019s now share a glimpse of Adam Selipsky\u2019s thinking. We\u2019ll extract and analyze statements from his sit-down with John Furrier. Each year, John has a one-on-one conversation with the AWS CEO. He has been doing this for 10 years and the outcome is a better understanding of the directional thinking of the leader of the No. 1 cloud platform. We\u2019ll first share some direct quotes, run through them with some commentary and then bring in some ETR data to assess the market implications.<\/p>\n

The cloud as a business transformer<\/h4>\n

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IT in general and data are moving from departments into becoming intrinsic parts of how businesses function.<\/p>\n<\/blockquote>\n

Our takeaway is the cloud generally and AWS specifically are moving toward deeper business integration.<\/p>\n

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In time we\u2019ll stop talking about people who have the word [data] analyst in their title. Rather, we\u2019ll have hundreds of millions of people who analyze data as part of their day-to-day job, most of whom will not have the word analyst anywhere in their title. We\u2019re talking about graphic designers and pizza shop owners and product managers\u2026 and data scientists as well.<\/p>\n<\/blockquote>\n

Our takeaway here is this speaks to democratizing data and fits with the data mesh trend.<\/p>\n

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Customers need to be able to take an end-to-end integrated view of their entire [data] journey from ingestion to storage to harmonizing the data to being able to query it, doing business intelligence and human-based analysis to being able to collaborate and share data\u2026 and we\u2019ve been putting together a broad suite of tools from database to analytics to business intelligence to help customers with that.<\/p>\n<\/blockquote>\n

Our takeaways: This last statement is true. However, under Jassy, there was not a lot of emphasis on an end-to-end integrated view. We believe it\u2019s clear from these statements that Selipsky is getting direct customer feedback that demands attention to this capability.<\/p>\n

Simplify, minimize data movement and inject intelligence into the data equation<\/h4>\n

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If you have data in one place you shouldn\u2019t have to move it every time you want to analyze that data. It would be much better if you could leave that data in place and avoid all the ETL, which has\u00a0 become a nasty three-letter word. More and more we\u2019re building capabilities where you can query that data in place.<\/p>\n<\/blockquote>\n

Our takeaway: This is a trend we see a lot in the marketplace. Oracle Corp. with MySQL Heatwave, the entire trend toward converged database, Snowflake Inc. and MongoDB Inc. extending their platforms into transactions and analytics respectively and so forth.<\/p>\n

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The other phenomenon is infusing machine learning into all those capabilities.<\/p>\n<\/blockquote>\n

Our takeaway: Yes, the comments from the Moschella graphic above come into play here. Organizations must put data at the core and they\u2019ll do so by deploying intelligent applications and infrastructure.<\/p>\n

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It\u2019s not a data cloud. It\u2019s not a separate cloud. It\u2019s a series of broad but integrated capabilities to help you manage the end-to-end lifecycle of your data.<\/p>\n<\/blockquote>\n

Our takeaway: This is a possible blind spot for AWS. In\u00a0Furrier\u2019s recent post<\/a>\u00a0on his interview with Selipsky, he cited commentary from Ali Ghodsi, CEO of Databricks Inc., and Dev Ittycheria, CEO of MongoDB. The notable delta is Selipsky sees these companies and the likes of Snowflake as independent software vendors . Both Ghodsi and Ittycheria see themselves as data platforms with a growing ecosystem of ISVs. In our view, they are building what we call superclouds on top of AWS and other clouds.<\/p>\n

Closing the data sharing gaps<\/h4>\n

Selipsky\u2019s next commentary that we\u2019ll highlight focused on data governance.<\/p>\n

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Data governance is a huge issue. Really what customers need is to find the right balance for their organization between access to data and control. And if you provide too much access, then you\u2019re nervous your data\u2019s going to end up in places that it shouldn\u2019t be, viewed by people who shouldn\u2019t be viewing it. And you feel like you lack security around that data. And by the way, what happens then is people overreact and lock it down so that almost nobody can see it.<\/p>\n<\/blockquote>\n

Our takeaway: Really well-put.\u00a0But today this is a gap for AWS in our view. It\u2019s not easy to share data in a safe way, especially outside your organization.<\/p>\n

Data clean rooms: That\u2019s a good idea<\/h4>\n

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\n[Data] Clean room is a really, really interesting area. And I think there\u2019s a lot of different industries in which clean rooms are applicable. I think that clean rooms are an interesting way of enabling multiple parties to share and collaborate on the data while completely respecting each party\u2019s rights and their privacy mandate.<\/p>\n<\/blockquote>\n

Our takeaway: This is also a gap within AWS today.\u00a0Sure, AWS has RedShift Data Sharing. But that capability only works across clusters for users within the same AWS account. To share data with users on other AWS accounts, they needed to extract it from one system and load it into another \u2013 the very process Selipsky alluded to that he\u2019s trying to eliminate.<\/p>\n

We know Snowflake is well down this path. And Databricks with Delta Sharing is also on this curve. So AWS has to address data sharing further and demonstrate the capability as part of its end-to-end data integration strategy.<\/p>\n

Is it possible AWS will pursue this vision with partners? That would be a powerful combination. But it\u2019s more likely AWS develop its own data sharing capability and continue down a separate partner path as it has in the past.<\/p>\n

AWS\u2019 end-to-end data vision = Snowflake + Databricks + IaaS<\/h3>\n

Let\u2019s bring in some ETR spending data to put some context around this issue with reference points in the form of AWS and its competitors and partners.<\/p>\n

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Above is a chart from ETR that shows Net Score or spending momentum on the Y axis and Overlap or pervasiveness in the survey on the X axis. The table insert informs as to how the dots are positioned, Net Score by shared Ns in the survey. We\u2019ve filtered the data on three big data segments: Analytics, database and ML\/AI. The red dotted line at 40% indicates highly elevated customer spend.<\/p>\n

As usual, Snowflake outperforms all players on the Y axis with a Net score of 63%. Databricks is right behind Snowflake with a robust 53% Net Score. All three big U.S. cloud players are above that line, with Microsoft Corp. and AWS dominating the X axis. And you see a number of other emerging data players such as Grafana Labs Inc. and Datadog Inc. MongoDB is solidly in the mix and then some more established players such as Splunk Inc., Tableau Software and Cisco Systems Inc. with its network observability offerings.<\/p>\n

Then you see the really established players in data such as Informatica Inc., IBM Corp. and Oracle, all with strong presence but red in the momentum category.<\/p>\n

We\u2019ve put a red highlight around Databricks, Snowflake and AWS. Why? Well, we feel there\u2019s no way AWS is going to hit the brakes on innovating at the base service level \u2013 what we called primitives earlier. Selipsky told Furrier as much in their sit-down. Our premise is that AWS will serve the technical user and data science community, the traditional domain of Databricks, and at the same time address the end-to-end integration, data sharing and business line requirements that Snowflake is positioned to serve. Meanwhile, both Snowflake and Databricks are pursuing a similar vision.<\/p>\n

Notably, people often ask us, \u201cHow will Snowflake and Databricks compete with the likes of AWS?\u201d And we answer, \u201cBy focusing on data exclusively and bringing multicloud to the table.\u201d But perhaps the more interesting question is: How will AWS compete with the likes of the specialists \u2013 Snowflake and Databricks \u2014 and continue to offer best-of-breed services?<\/p>\n

The answer is depicted above. AWS will serve both the technical developer and data science audience\u00a0and<\/em><\/strong>\u00a0through end-to-end integrations and future services that simplify the data journey for\u00a0 business users.<\/p>\n

Ecosystem is AWS\u2019 not-so-secret weapon<\/h4>\n

The nuance is in all the other dots in the data \u2014 and the hundreds or 100,000 that are not shown here, and that\u2019s the AWS ecosystem. You see, AWS has earned the status of the No. 1 cloud platform that everyone wants to partner with. It has more than 100,000 partners in 150 countries, and that ecosystem, combined with these capabilities we\u2019re discussing, while perhaps behind in areas such as data sharing and integrated governance, can wildly succeed by offering these capabilities and leveraging its ecosystem.<\/p>\n

For their part, the Snowflakes, Databricks and Mongos of the world have to stay focused on the mission, build the best products and develop their own ecosystems to compete for and attract the mindshare of both developers and business users. And that\u2019s why it\u2019s so interesting to hear Selipsky basically say these partners are (just) ISVs and this data capability is not a separate cloud, it\u2019s a set of integrated services.<\/p>\n

Meanwhile, Snowflake, Databricks and MongoDB (and others) in our view are building superclouds on top of AWS, Azure and Google.<\/p>\n

When great products meet great sales and marketing, good things can happen, so this will be really fun to watch what AWS announces in this area at re:Invent.<\/p>\n

The correlation between serverless and containers<\/h3>\n

One other topic Selpisky talked about was the relationship between serverless and container adoption.\u00a0And we have some data on this.<\/p>\n

But before we go there,\u00a0listen\u00a0<\/a>to\u00a0what Andy Jassy said in 2017<\/a>\u00a0on theCUBE talking about serverless and if Amazon had to start over it would build the platform on serverless:<\/p>\n

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In the very earliest days of AWS Jeff used to say a lot if I were starting Amazon today I have built-in on top of AWS. I think the same thing is true here with Lambda. I think if Amazon were starting today, it\u2019s a given they would build it on the cloud and I think with a lot of the applications that comprise Amazon\u2019s consumer business, we would build those on our serverless capabilities. We still have plenty of capabilities and features and functionality we need to add to Lambda, and our various services, so that may not be true from the get go right now. But I think if you look at the hundreds of thousands of customers who are building on top of Lambda and lots of real applications\u2026 people are building real serious things on top of Lambda. And the pace of iteration, you\u2019ll see there, will increase as well and I really believe that to be true over the next year or two.<\/p>\n<\/blockquote>\n

Jassy made that statement five years ago and gave a strong hint that serverless was going to be a key developer platform going forward. And Selipsky referenced the correlation between serverless and containers so we wanted to test that with the ETR data.<\/p>\n

Below is a\u00a0screen grab of that same XY view<\/a>\u00a0across 1,300 respondents in the October ETR survey. We\u2019ve isolated on the cloud computing sector and pulled the serverless options for the Big Three cloud players. You can see Google Cloud Functions, AWS Lambda and Microsoft Azure Functions. On the Y axis is Net Score which is a measure of spending momentum. On the X axis is presence in the data set.<\/p>\n

Now remember, anything above that 40% mark is considered highly elevated. All three serverless platforms are well above that level.<\/p>\n

So what we want to do is test the correlation between serverless adoption and momentum, which we\u2019re showing below with container adoption. And the way we do that is we then filter the data on containers to see what happens. And you\u2019ll see all three platforms move to the right. Now the N drops to 443 \u2013 i.e., the overlap between those aggressively adopting serverless and those adopting containers.<\/p>\n

The first chart is serverless platforms alone and the second chart shows serverless customers that are also adopting containers aggressively. And you can see in the latter chart all three serverless platforms move to the right, signifying a high correlation between the two.<\/p>\n

Serverless adoption generally<\/h4>\n

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Correlation between serverless and container adoption<\/h4>\n

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Why does this matter? Developer simplification and choice<\/h4>\n

To get a better understanding of what this means we reached out to friend and former CUBE co-host Stu Miniman. What he said was people generally used to think of virtual machines, containers and serverless as distinctly different architectures, but the lines are beginning to blur.<\/p>\n

Serverless makes things simpler for developers who don\u2019t want to worry about underlying infrastructure. As Selipsky and the data indicate, serverless and containers are coming together.<\/p>\n

But as we discussed with Miniman, there\u2019s a spectrum where on the left you have native cloud VMs, then in the middle AWS Fargate, and the rightmost anchor is Lambda.<\/p>\n

Using containers without serverless<\/h4>\n

Traditionally in the cloud, if you wanted to use containers, developers would have to build a container image, select and deploy EC2 images, allocate a certain amount of memory, fence off the apps in a virtual machine, run the EC2 instances against the app and then pay for all the EC2 resources.<\/p>\n

AWS Fargate \u2013 simpler but some control over the runtime<\/h4>\n

AWS Fargate lets you run containerized apps with less infrastructure management. So with Fargate, you\u2019d build the\u00a0container images, allocate your memory and compute resources, run the app and pay for the resources only when they\u2019re used. Fargate lets you control the runtime environment while at the same time simplifying the infrastructure management. And you don\u2019t have to worry about isolating the app and other stuff like choosing server types and patching. AWS does all that for you.<\/p>\n

AWS Lambda: Don\u2019t think about the underlying infrastructure<\/h4>\n

Then there\u2019s Lambda. With Lambda you don\u2019t have to worry about any of the underlying server infrastructure. You\u2019re just running code as functions. So the developers spend their time worrying about the applications and the functions needed.<\/p>\n

The point is there\u2019s a movement, as we saw in the ETR data, toward simplifying the development environment and allowing the cloud vendor, AWS in this case, to do more of the underlying management. Some folks will still want to turn knobs and dials, but increasingly we\u2019re going to see more higher-level service adoption.<\/p>\n

Re:Invent: always a firehose of content<\/p>\n

Let\u2019s do a rapid rundown of what to expect this year at re:Invent 2022.<\/p>\n

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We talked about\u00a0optimizing cloud spend<\/strong>\u00a0and that will undoubtedly be a conversation in the \u201cHallway Track.\u201d<\/p>\n

AWS will continue to deliver on the underlying core infrastructure, but we expect messaging around simplifying the experience for organizations.\u00a0Selipsky is leading AWS into its next phase of growth and that means moving beyond IT transformation into deeper business integration and organizational transformation. And so he\u2019s leading a multivector strategy \u2013 serving the traditional peeps who want fine-grained access to core services \u2013 we\u2019ll see continued innovation in compute, storage, AI and the like \u2014 and\u00a0simplification<\/strong>\u00a0through both integration and horizontal apps like Amazon Connect.<\/p>\n

As we\u2019ve reported many times, Databricks is moving from its stronghold realm of data science into business intelligence and analytics, whereas Snowflake is coming from its data analytics strength and moving into the world of data science.\u00a0AWS is going down a path of Snowflake meets Databricks with an underlying cloud IaaS and PaaS layer that puts these three companies on a very interesting trajectory. You can expect AWS to go right after the data sharing opportunity, and in doing so, it will have to address\u00a0data governance<\/strong>\u00a0in a more complete fashion.<\/p>\n

Price-performance<\/strong>\u00a0is a topic that will never go away. One thing we haven\u2019t mentioned today is silicon. It\u2019s a topic we\u2019ve covered\u00a0extensively on Breaking Analysis<\/a>\u00a0from Nitro to Graviton, the AWS acquisition of Annapurna, and new specialized capabilities such as Inferentia and Trainium. We\u2019d expect something more at re:Invent. Perhaps new Graviton instances. David Floyer said he\u2019s expecting a complete SoC \u2013 system-on-chip \u2013 from AWS at some point, an Arm-based server to eventually include high-speed CXL connections to devices and memories. That would be all to address next-gen applications, lower power requirements and lower cost.<\/p>\n

Swami Sivasubramanian will give his usual update on\u00a0ML and AI<\/strong>, building on Amazon\u2019s years of SageMaker innovation. Perhaps there will be a focus on conversational AI or better support for vision. And maybe better integration across Amazon\u2019s portfolio and integration of large language models or neural networks and generative AI.<\/p>\n

Security:<\/strong>\u00a0Always high on the list at re:Invent and of course there\u2019s re:Inforce, AWS\u2019 dedicated conference on security. Here we\u2019d like to see more on supply chain security and perhaps how AWS can help. As well as tooling to make the CISOs life easier. But the key so far is AWS is much more partner\/ecosystem-friendly in the security segment than Microsoft traditionally has been. As such, firms such as Okta Inc., CrowdStrike Holdings Inc. and Palo Alto Networks Inc. \u2013 and many others \u2013 have plenty of room to play in the AWS ecosystem.<\/p>\n

We\u2019d expect to hear something about\u00a0ESG<\/strong>\u00a0\u2014 environmental, social and governance \u2014 and hopefully how not only AWS is helping the environment \u2014 that\u2019s important \u2014 but also how they\u2019ll help customers save money.<\/p>\n

And finally \u2013 re:Invent is an\u00a0ecosystem<\/strong>\u00a0event.\u00a0It is the Super Bowl of tech events and the ecosystem will be out in full force. Every tech company on the planet will have a presence and theCUBE will be featuring many of the partners from the show floor.<\/p>\n

So you\u2019ll definitely want to tune into thecube.net and check out our exclusive re:Invent coverage. We start Monday evening and go wall-to-wall through Thursday. We have three sets at the show and our entire team will be there, so please reach out or stop by and say hello.<\/p>\n

Keep in touch<\/h3>\n

Many thanks to Stu Miniman and David Floyer for their input on today\u2019s episode. And of course John Furrier for extracting the signal from the noise in his sitdown with Adam Selipsky. Kudos to Alex Myerson and Ken Shiffman on production, podcasts and media workflows for Breaking Analysis. Special thanks to Kristen Martin and Cheryl Knight who help us keep our community informed and get the word out, and to Rob Hof, our editor in chief at SiliconANGLE.<\/p>\n

Remember we publish each week on\u00a0Wikibon<\/a>\u00a0and\u00a0SiliconANGLE<\/a>. These episodes are all available as\u00a0podcasts wherever you listen<\/a>.<\/p>\n

Email\u00a0david.vellante@siliconangle.com<\/a>, DM\u00a0@dvellante on Twitter<\/a>\u00a0and comment on\u00a0our LinkedIn posts<\/a>.<\/p>\n

Also, check out this\u00a0ETR Tutorial we created<\/a>, which explains the spending methodology in more detail.\u00a0Note:\u00a0ETR<\/a>\u00a0is a separate company from Wikibon and SiliconANGLE.<\/i>\u00a0If you would like to cite or republish any of the company\u2019s data, or inquire about its services, please contact ETR at legal@etr.ai.<\/p>\n

Here\u2019s the full video analysis:<\/p>\n