Some of the existing features of MySQL Autopilot, such as auto provisioning and auto query plan, have been improved to support better performance in the lakehouse service, the company said. Oracle’s MySQL HeatWave Lakehouse comes with support for MySQL Autopilot, which was launched in August 2021 as a component of the HeatWave portfolio, and uses machine learning to accelerate query performance and scalability. Machine learning-based automation with MySQL Autopilot The entire MySQL HeatWave portfolio has also been made available across multiple cloud service providers including Oracle Cloud Infrastructure (OCI), AWS and Microsoft Azure, Oracle said. “Any change made to the OLTP data is updated in real time and reflected in the query result,” the company said in a statement. The new service allows enterprises to query their online transaction processing ( OLTP) data stored inside MySQL database and combine it with data stored in the object store using standard MySQL syntax. This means that enterprises can use MySQL HeatWave even when their data is not stored inside a MySQL database. MySQL HeatWave Lakehouse, the latest addition to Oracle’s MySQL HeatWave cloud service for analytics and mixed workloads, will allow enterprises to process and query data across file formats, such as CSV and Parquet, as well as Aurora and Redshift backups from AWS, the company said. Lakehouse provides support for multiple file formats More than half (53%) the participants in a Ventana Research's Analytics & Data Benchmark Research poll said they are using object storage in their analytics efforts, the market research firm said, adding that a further 29% are evaluating or planning to do so. This is all the more relevant for semistructured and unstructured data that is unsuitable for storing and processing in a data warehouse, Aslett explained.
Data lakes have gained significance since the time vendors started offering a cloud object storage as the underlying data repository, which makes the lake concept a relatively inexpensive way of storing large volumes of data from multiple enterprise applications and workloads.
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