Oracle is adding new machine learning features to its data analytics cloud service MySQL HeatWave. MySQL HeatWave combines OLAP (online analytical processing), OLTP (online transaction processing), machine learning, and AI-driven automation in a single MySQL database. The new machine learning capabilities will be added to the service’s AutoML and MySQL Autopilot components as mentioned in the company’s blog by Nipun Agarwal – Senior Vice President, MySQL HeatWave. The company has also announced new capabilities for MySQL HeatWave on AWS and price-performance improvements for MySQL HeatWave on OCI.
New MySQL HeatWave AutoML capabilities
MySQL HeatWave provides native, in-database machine learning. Customers don’t need to move data to a separate machine learning service, they can easily and securely apply machine learning training, inference, and explanation to data stored inside MySQL HeatWave. HeatWave AutoML automates the machine learning lifecycle. Benchmarks demonstrate that, on average, HeatWave AutoML produces more accurate results than Amazon Redshift ML, trains models 25X faster, and scales as more nodes are added. It is available at no additional cost to MySQL HeatWave customers.
So far, customers could automatically train regression, classification, and univariate time series forecasting models. Oracle has now announced the extension of MySQL HeatWave AutoML’s lifecycle automation to support for multi-variate time series forecasting, unsupervised anomaly detection, and recommender systems. These capabilities are not offered by other competitive cloud database services like Redshift and Snowflake. The machine learning capabilities are now available via an interactive console that makes it easier for business analysts to build, train, run, and explain ML models without help from IT.
HeatWave enhances anomaly detection
Along with multivariate time series forecasting, Oracle is adding machine-learning based “unsupervised” anomaly detection to MySQL HeatWave.
In contrast to the practice of using specific algorithms to detect specific anomalies in data, AutoML can detect different types of anomalies from unlabeled data sets, the company said, adding that this feature helps enterprise users when they don’t know what anomaly types are in the dataset.
“The model generated by HeatWave AutoML provides high accuracy for all types of anomalies — local, cluster, and global. The process is completely automated, eliminating the need for data analysts to manually determine which algorithm to use, which features to select, and the optimal values of the hyperparameters,” said Agarwal.
In addition, AutoML has added a recommendation engine, which it calls recommender systems, that underpins automation for algorithm selection, feature selection, and hyperparameter optimization inside MySQL HeatWave.
Optimizing HeatWave on AWS
While MySQL HeatWave began as an Oracle Cloud-only offering, Oracle last year expanded the availability to Amazon AWS. The AWS implementation of HeatWave is optimized for the specifics of the AWS infrastructure, tightly integrating with Amazon’s S3, CloudWatch, and PrivateLink features.
MySQL Heatwave improves on that integration this week, announcing a new optimized storage layer built on S3 for hybrid column representation. When data is loaded from MySQL into Heatwave, a copy is made to the scale-out data management layer built on S3. If reloading is needed, the data can be loaded without being transformed, resulting in significantly faster recovery times and availability, Oracle said. Plus, the data never leaves the AWS cloud, eliminating withdrawal fees.
New optimized storage layer improves reload performance, delivering impressive results. Oracle provided performance numbers that show MySQL Heatwave on AWS performs up to 20 times faster than Amazon Redshift and 16 times faster than Snowflake at the same price.
MySQL HeatWave on OCI
Introducing a new small shape for HeatWave
Until now, the size of a HeatWave node was 512GB. Many customers with smaller data sizes expressed the desire to use HeatWave without provisioning such a large node, so Oracle has now introduced a new small 32GB shape. It can process up to 50GB of data and only costs $16 per month.
Improved price performance
The amount of data that can be processed by a HeatWave node (512GB) is now increased to 1TB from 800GB (note that the exact amount of data processed depends upon the data and workload characteristics). With this increase and other query performance improvements, the price performance benefit of HeatWave has further increased by 15%.
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