Oracle HeatWave

The Six Five team dives into Oracle HeatWave.

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Patrick Moorhead: Daniel, let’s move to the next topic, Oracle HeatWave, first of all, what is HeatWave and what’s new about it?

Daniel Newman: Absolutely. So, let’s start at the high level. This goes back to a decade ago when Sun Microsystems was acquired by Oracle, which was MySQL. Since then, Oracle’s kept MySQL relatively distinct. About 15 months ago, Oracle released what’s called MySQL HeatWave and it’s basically the company’s own optimized implementation of MySQL running on Oracle’s OCI infrastructure. For those of you not familiar, Pat and I have talked a lot about a very bullish sentiment on Oracle cloud. This is MySQL running on Oracle’s public cloud platform.

Basically what happened this week is a third release of HeatWave, scaling up node size, reducing costs for certain workloads, and introducing probably what was most notable here, and that is the in-database machine learning. Our team is currently in the process of research, a brief that is going to come out on this. So, I had the chance to review break it down and here’s what I think is most interesting about this particular iteration of the MySQL launch and moving into ML.

First of all, they’ve added, it’s native support for machine learning. It’s full automation, it takes advantage of the autonomous database and technologies that Oracle has been pushing very aggressively. It’s explainable AI, which is becoming an increasingly important topic right now as we’re looking for more transparency. So, all models in HeatWave ML are going to be explainable. They’re differentiated, therefore as well.

But also probably what’s going to be most attractive about it, is the improved performance, quality, repeatability of the explanations and of course, some of the scaling. So, you have things like scaling with cluster size, real-time elasticity, more data per node.

So, these were some of the breakdowns that our team identified as key differentiators. As we went through, by the way, Oracle did a bunch of benchmarking. So, I’m going to comment on their benchmarking just a little bit, but I also want to be very clear that commenting put Oracle clearly ahead of competitors, Amazon, Google, and others in this space. Having said that, these are benchmarks that require … could opportunistically be challenged by the other companies Pat, but at this point … and you got to wonder, because by the way in the second release of HeatWave, there was also some very strong benchmarking that was released and none of the competitors have stepped up and done any benchmarking or done any … [inaudible] say, publicly debated these strong numbers. So, you got to wonder Redshift or if BigQuery felt that they were able to outperform and it was going to be clearly measurable. You’d think they would’ve done those benchmark tests and put those into the market and they haven’t.

As we assessed the conclusions and recommendations, we basically found that across the cloud DB market, that the value was pretty tremendous, the price value for the technology. Had a hard to beat feature set, auto ML, scalability, real-time elasticity, the [inaudible] data per node, and overall the portfolio-wide enhancements that they were able to do. But more so than anything, Pat, it was the price performance that we really walked away from.

We also found that the benchmarks demonstrated clear out-performance this current time against Redshift, Snowflake, Synapse, and BigQuery. All of them, according to the data that we were able to review, were both more expensive and none of them had the same level of performance, which we think warrants that the companies that are in review cycles right now probably need to spend some time evaluating what HeatWave is offering.

By the way, very strong. I mean, Oracle’s always been a database company, Pat, but this is one of those things that’s truly showing how they’re taking their infrastructure and their database, the legacy and engineering prowess they’ve had in database for a long time, and putting it together to offer a significant differentiation in market, layering in autonomous database capabilities, competing at scale against major public cloud players and finding something that’s differentiated. So, a very good release, Pat, and pretty impressive numbers.

Patrick Moorhead: Good analysis there, Daniel, by the team. Our analyst, Matt Kimble wrote a note as well, that I’ll put in the show notes and essentially this is, so, HeatWave’s incredible performance has a lot to do with its all-in memory, where other solutions are spooling out back and forth to disk. So architecturally, that gives HeatWave a big advantage on performance, pretty much versus anything that touches a disk. So, that doesn’t surprise me.

Like you said, I didn’t see Oracle HeatWave competitors saying that these benchmarks were wrong either. So, I think they know. This next step, this was the first update in, I think 15 months, added machine learning in the database at no extra cost. So, even though Oracle’s a premium provider, them being able to essentially commoditize a machine learning, is pretty fun. Whether that’s with auto ML, which is key and that doesn’t mean that it’s just magic and you don’t have to do anything, but auto ML makes ML a heck of a lot easier. You don’t have to be necessarily a data science to have it happen.

So, HeatWave ML, not only is it higher performance, because you actually have the machine learning inference in the database, but it’s also basically free. So, once again, on architecture play on something that I think is pretty important. I love the idea of a premium vendor like Oracle commoditizing ML. I know exactly who this is targeted at. It’s 100% targeted at Redshift and how you architect Redshift and get machine learning out of that. So, check out the reports from both of our companies, if you want some of the gory details.

Daniel Newman: Yeah. The details are good. I’m not boomeranging per se here, Pat, but I do love the fact that you did mention the in-memory, because that is such an important differentiator. So, I’m glad you caught that out because I don’t think I mentioned it. So anyhow, good stuff there.

Patrick Moorhead: Listen, this is why we both talk about this, Daniel. If we were just repeat each other, how interesting of a show would that be? It’d be a snoozer.

Daniel Newman: Yeah, companies might like it, but I don’t think that people watching, trying to get the analysis would be all that excited about it.

Patrick Moorhead: Exactly.

Daniel Newman: Want to keep moving?

Patrick Moorhead: Yeah, let’s do that. Yeah.

Author Information

Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.

From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.

A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.

An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.


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