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Since Oracle launched its MySQL HeatWave service in December 2020, it has continuously driven differentiation in the database-as-a-service market. While competing against some of the biggest names in the cloud space, the company has shown what appears to be “too good to be true” price-for-performance against competitors such as Snowflake.

With the latest update to MySQL HeatWave, Oracle shows no signs of slowing down. And I think this service may be the “killer app” for Oracle Cloud Infrastructure (OCI)—or even Oracle as a whole. I’ll explain my reasoning in this article.

MySQL HeatWave changed the analytics game

Most IT folks who have spent time around databases understand the pervasiveness of MySQL in the enterprise. Since it was released under the GNU General Public License (GPL) in 2000, the database platform has exploded in popularity among companies of all sizes, from smaller organizations that want a SQL database without the considerable cost, to enterprise organizations deploying large departmental databases. My first exposure to the platform came in 2003, when I worked as an IT executive in a large state government organization. We used MySQL everywhere possible to lower costs without sacrificing performance and functionality.

Over the years, MySQL became the world’s most popular open-source database and the second-most popular database overall (after Oracle Database). Yes, while a lot of the buzz is for the likes of MongoDB and other “cool” NoSQL platforms, the top two database distributions—by far—are owned by Oracle. And they are both SQL-based.

The challenge I saw when running a state IT organization is the same challenge that IT orgs have been facing ever since: data silos and the fact that MySQL is not optimized for analytics. When a business has hundreds or even thousands of MySQL instances, integrating all of that data and gleaning insights from it via analytics is painful. All too often, business users must rely on IT to perform time-consuming and costly—yet error-prone! —extract, load and transform (ETL) processes to bring all the data to one central location for analysis. Or brave business users might attempt this themselves, then reach out to IT a few weeks later when they’ve given up trying. By that time, they’re analyzing old data.

The MySQL development team at Oracle recognized this challenge impacting customers, and MySQL HeatWave was born. The idea was simple: deploy a cloud service whereby customers of all sizes could run real-time analytics on all of their data—both transactional and historical—and enable it with “point-and-click” simplicity, and without needing the dreaded ETL.The challenge I saw when running a state IT organization is the same challenge that IT orgs have been facing ever since: data silos and the fact that MySQL is not optimized for analytics. When a business has hundreds or even thousands of MySQL instances, integrating all of that data and gleaning insights from it via analytics is painful. All too often, business users must rely on IT to perform time-consuming and costly—yet error-prone! —extract, load and transform (ETL) processes to bring all the data to one central location for analysis. Or brave business users might attempt this themselves, then reach out to IT a few weeks later when they’ve given up trying. By that time, they’re analyzing old data.

The MySQL development team at Oracle recognized this challenge impacting customers, and MySQL HeatWave was born. The idea was simple: deploy a cloud service whereby customers of all sizes could run real-time analytics on all of their data—both transactional and historical—and enable it with “point-and-click” simplicity, and without needing the dreaded ETL.

When I look at MySQL HeatWave, I consider two things—the richness of features and the ability of an average user to take advantage of these capabilities. From both perspectives, I’m impressed. As touched on above, the performance numbers are almost too good to be true. More than that, using this service is simple: no refactoring or rearchitecting of applications, no new analytics or visualization tools to learn. Just point the tool in the right direction, click a couple of times, and you have a database environment that supports online transactional processing (OLTP) and analytics.

MySQL Autopilot drives real automated operations

Some product manager once said, “You never know how your product is going to perform until it’s in the hands of paying customers.” Maybe that product manager was me in a previous part of my career. In any case, this truism is obvious to anyone who has ever launched a product.

When Oracle launched its first update to the MySQL HeatWave service in mid-2021, it focused on automating the data management lifecycle using machine learning (ML). In fact, MySQL HeatWave Autopilot was the first service I saw that automated many DBA functions that would previously consume hours a week.

Database tuning is an art form requiring both technical depth and something like clairvoyance. Deploying and provisioning databases is hard enough but tuning them is a never-ending process—one that can consume database professionals. To alleviate this, MySQL Autopilot combined deep analytics and finely tuned ML models to drive a continually optimized and always resilient MySQL database environment.

This update of MySQL HeatWave is also where I started to pay attention to Oracle’s competitive performance comparisons. Once again, the numbers initially seemed too good to be true. Suffice it to say that HeatWave outperformed every major cloud provider in analytics.

Of special note was its performance relative to the very popular Snowflake. When running TPC-H (a decision support benchmark), MySQL HeatWave showed an incredible 35x price/performance advantage over Snowflake. In terms of raw performance, HeatWave had a 6.8x advantage, and in terms of price, a 5.2x advantage. Pretty compelling, right? Benchmarks can be manipulated—or so I would think. Except that Oracle publishes its test harness on GitHub so customers can see for themselves. That shows real confidence in their capabilities.

HeatWave AutoML and multi-cloud—a natural next step

After introducing MySQL Autopilot, Oracle’s next big act with HeatWave was the integration of ML into MySQL HeatWave for model training purposes—aptly named HeatWave AutoML. This gave HeatWave the ability to automate the building, training, tuning, and explaining of ML models in real-time based on the data residing in MySQL. Again, HeatWave is democratizing machine learning by enabling this functionality for all, so that companies that don’t have teams of data scientists or TensorFlow developers can gain the same kinds of insights and automation usually reserved for larger organizations.

Additionally, Oracle embraced the notion of multi-cloud by releasing MySQL HeatWave on AWS. That means the entire MySQL HeatWave environment—core capabilities, Autopilot and ML—is available for customers to use on AWS. Oracle did this to bring the value of MySQL HeatWave to customers already using AWS who might otherwise be priced out of the service because of data egress fees. (To use HeatWave in-memory analytics, you would have to move all your data from AWS into OCI.) So, rather than force this difficult decision, Oracle stood up the HeatWave service natively—including the control plane, data plane, and console— to run on AWS. Is performance as good as it would be on OCI? No. But it’s still really good. And there is no sense that Oracle is delivering an underpowered product to ultimately convince customers to move to OCI.

If your data resides on Microsoft Azure instead, life is equally easy for you. Because Oracle and Microsoft have deployed a low-latency layer-2 network connection, you can simply use HeatWave on OCI while your applications still reside in Azure. No latency hit, no cost-prohibitive ingress and egress fees.

As an analyst who was also an Oracle customer for some time, I feel like I’m witnessing a new company with a new attitude. In the past, Oracle was not known for its emphasis on making life easy for customers. In comparison to that, its trajectory with MySQL HeatWave is a breath of fresh air.

MySQL HeatWave’s latest release: more ML goodness

In the latest update to HeatWave, the team at Oracle has doubled down on ML by driving usability and automation. The democratization of ML only really happens when ML functions are practically available to all—meaning that they don’t require a team of developers and data scientists. And Oracle has delivered even further on that promise with its latest release.

The result of Oracle’s work is automated machine learning that is highly performant and fully automated. And here’s what’s interesting: because of the highly parallelized architecture of MySQL HeatWave AutoML, these models are running on commodity CPUs. This drives down costs considerably—savings that are passed on to customers. So that price performance advantage I discussed earlier? Now it’s even better.

In addition to focusing on usability, Oracle has delivered three new ML capabilities in this latest update that are worth quickly highlighting.

1. Unsupervised anomaly detection. This feature identifies events deviating from the norm using a single algorithm. Banks looking to detect fraud, operational technology organizations looking for IoT sensor outliers, and cybersecurity teams focused on intrusion detection are all use cases that would benefit from this ability. HeatWave is the only solution to perform this detection at the local, global, and cluster levels with a single algorithm and a fully automated process. This makes it faster and more accurate than manually selecting, testing, and tuning multiple individual algorithms.
2. Recommender system availability. This capability automates the use of recommender systems—which recommend movies to watch and products to buy—in HeatWave, driving significantly faster training time coupled with a lower error rate (as compared with a public-domain solution). By comparison, other services only recommend algorithms, putting the burden on users to select the most appropriate one, and then manually tune it.
3. Multivariate time series forecasting. This feature automates the process for companies to accurately predict time series observations based on a number of variables. This would apply, for instance, if a power company is trying to determine seasonal electricity demands when considering other energy sources, weather, and so on. What once would require a team of data scientists is now just a few mouse clicks away.

Additionally, Oracle has made enhancements to MySQL Autopilot that further improve ML automation in a workload-aware way. Whether an organization is using HeatWave for OLTP, OLAP, or data lakehouse purposes, Autopilot removes mundane and tedious chores from the hands of administrators.

If the above graphic looks familiar, it is an update covering available Autopilot capabilities. The MySQL team at Oracle has added considerable functionality as MySQL Autopilot has evolved into a data management platform.

MySQL moves to a Lakehouse

Last October at CloudWorld 2022 Oracle announced MySQL HeatWave Lakehouse (currently in beta), continuing to extend HeatWave’s database capabilities. It enables customers to process and query hundreds of terabytes of data in object store in a variety of file formats, such as CSV and Parquet, as well as Aurora and Redshift export files.

Keeping with tradition, MySQL HeatWave Lakehouse delivers significantly better performance than competitive cloud database services for running queries (17X faster than Snowflake) and loading data (2.7X faster than Snowflake) on a 400TB TPC-H benchmark.

In addition, in a single query, customers can query transactional data in the MySQL database and combine it with data in the object store using standard MySQL syntax. New MySQL Autopilot capabilities that improve performance and make MySQL HeatWave Lakehouse easy to use also became available, increasing administrators’ productivity.

Is MySQL HeatWave really Oracle’s killer app?

Maybe I was being a little hyperbolic with my blog title. But MySQL HeatWave was revolutionary from the moment Oracle released it in late 2020. And it continues to separate itself from competing, but single-focus, database cloud solutions as Oracle adds functionality. MySQL HeatWave is evolving from an OLTP + in-memory analytics tool into something much bigger—if not a data management platform, then a fully featured OLTP, data analytics, machine learning, and lakehouse platform within one integrated database.

My only question is, what’s next? Considering how quickly Oracle has been innovating with HeatWave, I don’t think it will take long to find out.