 From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. Database is the heart of enterprise computing. The market is both exploding and it's evolving. The major forces transforming the space include cloud and data, of course, but also new workloads, advanced memory and IO capabilities, new processor types, a massive push towards simplicity, new data sharing and governance models and a spate of venture investment. Snowflake stands out as the gold standard for operational excellence and go-to-market execution. The company has attracted the attention of customers, investors and competitors and everyone from entrenched players to upstarts once in and the act. Hello everyone and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we'll share our most current thinking on the database marketplace and dig into Snowflake's execution. Some of its challenges and we'll take a look at how others are making moves to solve customer problems and try to get a piece of the growing database pie. Let's look at some of the factors that are driving market momentum. First, customers want lower license costs, they want simplicity, they want to avoid database sprawl, they want to run anywhere and manage new data types. These needs often are divergent and they pull vendors and technologies in different direction. It's really hard for any one platform to accommodate every customer need. The market is large and it's growing. Gartner has it at around 60 to 65 billion with a cagger of somewhere around 20% over the next five years. But the market as we know it is being redefined. Traditionally, databases have served two broad use cases, OLTP or transactions and reporting like data warehouses but a diversity of workloads and new architectures and innovations have given rise to a number of new types of databases to accommodate all these diverse customer needs. Many billions have been spent over the last several years in venture money and it continues to pour in. Let me just give you some examples. Snowflake prior to its IPO raised around 1.4 billion. Redis Labs has raised more than half a billion dollars so far, Cockroach Labs more than 350 million, Couchbase 250 million, Single Store formerly MemSQL 238 million, Yellowbrick data 173 million. And if you stretch the definition of database a little bit to include low code or no code, AirTable has raised more than 600 million and that's by no means a complete list. Now, why is all this investment happening? Well, in a large part it's due to the TAM. The TAM is huge and it's growing and it's being redefined. Just how big is this market? Let's take a look at a chart that we've shown previously. We use this chart to describe Snowflake's TAM and it focuses mainly on the analytics piece but we'll use it here to really underscore the market potential. So the actual database TAM is larger than this we think. Cloud and cloud data technologies have changed the way we think about databases. Virtually 100% of the database players that are in the market have pivoted to a cloud first strategy and many like Snowflake they're pretty dogmatic and have a cloud only strategy. Databases have historically been very difficult to manage. They're really sensitive to latency so that means they require a lot of tuning. Cloud allows you to throw virtually infinite resources on demand and attack performance problems and scale very quickly minimizing the complexity and tuning nuances. This idea, this layer of data as a service we think of it as a staple of digital transformations. It's this layer that's forming to support things like data sharing across ecosystems and the ability to build data products or data services. It's a fundamental value proposition of Snowflake and one of the most important aspects of its offering. Snowflake tracks a metric called edges which are external connections in its data cloud and it claims that 15% of its total shared connections are edges and that's growing at 33% quarter on quarter. This notion of data sharing is changing the way people think about data. We use terms like data as an asset. This is the language of the 2010s. We don't share our assets with others, do we? No, we protect them, we secure them, we even hide them but we absolutely don't want to share those assets but we do want to share our data. We had a conversation recently with Forrester analyst Michelle Getz and we both agreed we're going to scrub data as an asset from our phraseology. Increasingly, people are looking at sharing as a way to create, as I said, data products or data services which can be monetized. This is an underpinning of Jomak Dagani's concept of a data mesh. Make data discoverable, shareable and securely governed so that we can build data products and data services that can be monetized. This is where the TAM just explodes and the market is redefining and we think is in the hundreds of billions of dollars. Let's talk a little bit about the diversity of offerings in the marketplace. Again, databases used to be either transactional or analytic. The bottom lines and top lines in this chart here describe those two but the types of databases you can see in the middle of mushroomed just looking at this list. Blockchain is of course a specialized type of database and it's also finding its way into other database platforms. Oracle is notable here. Document databases that support JSON and graph data stores that assist in visualizing data. Inference from multiple different sources. That's one of the ways in which ad tech has taken off and been so effective. Key value stores, log databases that are purpose built. Machine learning to enhance insights. Spatial databases to help build the next generation of products, the next automobile. Streaming databases to manage real time data flows and time series databases. You know, we might have missed a few. Let us know if you think we have but this is a kind of pretty comprehensive list that is somewhat mind boggling when you think about it. And these unique requirements, they've spawned tons of innovation and companies. Here's a small subset on this logo slide and this is by no means an exhaustive list but you have these companies here which have been around forever like Oracle and IBM and Teradata and Microsoft. These are kind of the tier one relational databases that have matured over the years and they've got properties like atomicity, consistency, isolation, durability, what's known as acid properties, acid compliance. Some others that you may or may not be familiar with yellow brick data, we talked about them earlier. It's going after the best price performance in analytics and optimizing to take advantage of both hybrid installations and the latest hardware innovations. Single store, as I said, formerly known as MemSQL is a very high end analytics and transaction database, supports mixed workloads, extremely high speeds. We're talking about trillions of rows per second that can be ingested and queried. Couch base with hybrid transactions and analytics, Redis Labs, open source NoSQL doing very well as is Cockroach with distributed SQL, MariaDB with it's managed MySQL, Mongo and document database has a lot of momentum, EDB which supports open source Postgres. And if you stretch the definition a bit, Splunk for log database, why not? Chaos search, really interesting startup that leaves data in S3 and is going after simplifying the Elk stack. New Relic, they have a purpose built database for application performance management. Probably could have even put Workday in the mix as it developed a specialized database for its apps. Of course, we can't forget about SAP with HANA trying to pry customers off of Oracle and then the big three cloud players, AWS, Microsoft and Google with extremely large portfolios of database offerings. The spectrum of products in this space is very wide. You got AWS, which I think we're up to like 16 database offerings all the way to Oracle which has like one database to do everything not withstanding MySQL because it owns MySQL got that through the Sun acquisition and it recently made some innovations there around the heat wave announcement but essentially Oracle is investing to make its database Oracle database run any workload while AWS takes the approach of the right tool for the right job and really focuses on the the primitives for each database. Lot of ways to skin a cat in this enormous and strategic market. Okay, so let's take a look at the spending data for the names that make it into the ETR survey not everybody we just mentioned will be represented because they may not have quite the market presence of the ends in the survey but ETR the capture a pretty nice mix of players. So this chart here, it's one of the favorite views that we like to share quite often. It shows the database players across the 1500 respondents in the ETR survey this past quarter and it measures their net score. That's spending momentum and it's shown on the vertical axis and market share which is the pervasiveness in the data set is on the horizontal axis. Now Snowflake is notable because it's been hovering around 80% net score since the survey started picking them up. Anything above 40% that red line there is considered by us to be elevated. Microsoft and AWS, they also stand out because they have both market presence and they have spending velocity with their platforms. Oracle is very large but it doesn't have the spending momentum in the survey because nearly 30% of Oracle installations are spending less whereas only 22% are spending more. Now as a caution, this survey doesn't measure dollars spent and Oracle will be skewed toward the big customers with big budgets. So you got to consider that caveat when evaluating this data. IBM is in a similar position although its market share is not keeping up with Oracle's. Google, they got great tech especially with BigQuery and it has elevated momentum. So not a bad spot to be in although I'm sure we'd like to be closer to AWS and Microsoft on the horizontal axis. So it's got some work to do there. And some of the others we mentioned earlier like MemSQL, Couchbase. MemSQL is shown as a single, well shown as MemSQL here, they're now single store. Couchbase, Redis, Mongo, MariaDB, all very solid scores on the vertical axis. Cloudera just announced that it was selling to private equity and that will hopefully give it some time to invest in this platform and get off the quarterly shot clock. MapR was acquired by HPE and it's part of HPE's Esmerell platform, their data platform which doesn't yet have the market presence in the survey. Now, something that is interesting in looking at in Snowflake's earnings last quarter is its laser focus on large customers. This is a hallmark of Frank Slutman and Mike Scarpelli who I know, I know they don't have a playbook but they certainly know how to go whale hunting. So this chart isolates the data that we just showed you to the global 1000. Note that both AWS and Snowflake go up higher on the X-axis meaning large customers are spending at a faster rate for these two companies. The previous chart had an end of 161 for Snowflake and a 77% net score. This chart shows the global 1000 and the end there for Snowflake is 48 accounts and the net score jumps to 85%. We're not going to show it here but when you isolate the ETR data, it's nice, you can just cut it. When you isolate it on the Fortune 1000, the end for Snowflake goes to 59 accounts in the data set and Snowflake jumps another 100 basis points in net score. When you cut the data by the Fortune 500, the Snowflake end goes to 40 accounts and the net score jumps another 200 basis points to 88%. And when you isolate on the Fortune 100 accounts, there's only 18 there but still 18, their net score jumps to 89%, almost 90%. So it's very strong confirmation that there's a proportional relationship between larger accounts and spending momentum in the ETR data set. So Snowflake's large account strategy appears to be working. And because we think Snowflake is sticky, this probably is a good sign for the future. Now we've been talking about net score. So it's a key measure in the ETR data set. So we'd like to just quickly remind you what that is and use Snowflake as an example. This wheel chart shows the components of net score. That lime green is new adoptions. 29% of the customers in the ETR data set that are new to Snowflake, that's pretty impressive. 50% of the customers are spending more. That's the forest green, 20% are flat, that's the gray and only 1% the pink are spending less. And 0%, 0 are replacing Snowflake, no defections. What you do here to get net scores, you subtract the red from the green and you get a net score of 78%, which is pretty sick and has been sick as in good sick and has been steady for many, many quarters. So that's how the net score methodology works. And remember, it typically takes Snowflake customers many months, like six to nine months to start consuming its services at the contracted rate. So those 29% new adoptions, they're not going to kick into high gear until next year. So that bodes well for future revenue. Now, it's worth taking a quick snapshot at Snowflake's most recent quarter. There's plenty of stuff out there that you can Google and get a summary, but let's just do a quick rundown. The company's product revenue run rate is now at 856 million. It'll surpass a billion dollars on a run rate basis this year. The growth is off the charts, very high net revenue retention. We've explained that before with Snowflake's consumption pricing model. They have to account for retention differently than what a SaaS company. Snowflake added 27 net new million dollar accounts in the quarter and claims to have more than a hundred now. It also is just getting its act together overseas. Slutman says he's personally going to spend more time in Europe given his belief that the market is huge and they can disrupt it. And of course he's from the continent. He was born there and lived there and gross margins expanded due in a large part to renegotiation of its cloud costs. We're going to come back to that in a moment. Snowflake's also moving from a product led growth company to one that's more focused on core industries. Interestingly media and entertainment is one of the largest along with financial services and several others. To me this is really interesting because Disney's example that Snowflake often puts in front of its customers as a reference and it seems to me to be a perfect example of using data and analytics to both target customers and also build so-called data products through data sharing. Snowflake has to grow its ecosystem to live up to its lofty expectations and indications are that large SIs are leaning in big time Deloitte crossed a hundred million dollars in deal flow in the quarter. And the balance sheet's looking good. Thank you very much with $5 billion in cash. You know the snarks are going to focus on the losses but this is all about growth. This is a growth story. It's about customer acquisition. It's about adoption. It's about loyalty and it's about lifetime value. Now, as I said at the IPO and I always say this to young people don't buy a stock at the IPO. There's probably almost always going to be better buying opportunities ahead. Not always right about that, but I often am. Here's a chart of Snowflake's performance inside PO and I have to say it's held up pretty well. It's trading above its first day close and as predicted there were better opportunities than day one but you have to make a call from here. I mean, don't take my stock advice. Do your research. Snowflake, they're priced at perfection so any disappointment is going to be met with selling. You saw that the day after they beat their earnings last quarter because their guidance and revenue growth wasn't in the triple digits. It sort of moderated down to the 80% range and they pointed out, they pointed to a new storage compression feature that will lower customer cost and consequently it's going to lower their revenue. I swear, I think that before earnings call, Scarpelli sits back and says, okay, what kind of creative way can I introduce the dampened enthusiasm for the guidance? Now I'm not saying lower storage costs won't translate into lower revenue for a period of time but look, a drop in storage prices, customers are always going to buy more. That's the way the storage market works. And Snowflake did allude to that in all fairness. Okay, let me introduce something that people in Silicon Valley are talking about. And that is the cloud paradox for SaaS companies. You know, what is that? I was in a clubhouse room with Martin Casado of Andreessen when I first heard about this. He wrote an article with Sarah Wong calling it to question the merits of SaaS companies sticking with cloud at scale. Now the basic premise is that for startups in early stages of growth, the cloud is a no-brainer for SaaS companies but at scale, the cost of cloud, the cloud bill, approaches 50% of the cost of revenue and becomes an albatross that stifles operating leverage. Their conclusion ended up saying that as much as, perhaps as much as the back of the napkin, they admitted that, but perhaps as much as a, half a trillion dollars in market cap is being vacuumed away by the hyperscalers that could go to the SaaS providers as cost savings from repatriation. And that cloud repatriation is an inevitable path for large SaaS companies at scale. I was particularly interested in this as I had recently put out a post on the cloud repatriation myth. I think in this instance there's some merit to their conclusions, but I don't think it necessarily bleeds into traditional enterprise settings. But for SaaS companies, you know, maybe service now has it right running their own data centers or maybe a hybrid approach to hedge bets and save money down the road is prudent. What caught my attention in reading through some of the Snowflake docs like the S1 and its most recent 10K were comments regarding long-term purchase commitments and non-cancelable contracts with cloud companies. In the company's S1, for example, there was disclosure of $247 million in purchase commitments over a five plus year period. In the company's latest 10K report, that same line item jumped to 1.8 billion. Now Snowflake is clearly managing these costs as it alluded to in its earnings call. But one has to wonder at some point, will Snowflake follow the example of say Dropbox, which Andreessen used in his blog and start managing its own IT? Or will it stick with the cloud and negotiate hard? Snowflake certainly has the leverage. It has to be one of Amazon's best partners and customers even though it competes aggressively with Redshift. But on the earnings call, CFO Scarpelli said that Snowflake was working on a new chip technology to dramatically increase performance. What the heck does that mean? Is Snowflake not becoming a hardware company? So I'm going to have to dig into that a little bit and find out what that means. I'm guessing it means that it's taking advantage of ARM-based processors like Graviton, which many ISVs are allowing their software to run on that lower cost platform. Or maybe there's some deep dark in the weed secret going on inside Snowflake, but I doubt it. Okay, we're going to leave all that for there for now to keep following this trend. So it's clear just in summary that Snowflake, they're the pace setter in this new exciting world of data, but there's plenty of room for others. And they still have a lot to prove. For instance, one customer in an ETR CTO round table expressed skepticism that Snowflake will live up to its hype because its success is going to lead to more competition from well established players. This is a common theme. You hear it all the time. It's pretty easy to reach that conclusion. But my guess is it's the exact type of narrative that fuels Sluteman and sucked him back in to this game of thrones. Okay, that's it for now, everybody. Remember, these episodes are all available as podcasts wherever you listen. All you got to do is search Breaking Analysis Podcast and please subscribe to the series. Check out ETR's website at ETR.plus. We also publish a full report every week on wikibon.com and siliconangle.com. You can get in touch with me. Email is david.valante at siliconangle.com. You can DM me at dvalante on Twitter or comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights, powered by ETR. Have a great week, everybody. Be well and we'll see you next time.