Oracle has announced the general availability of its MySQL Heatwave Lakehouse service, marking the company’s entry into the data lakehouse business. The Heatwave Lakehouse service is a managed database-as-a-service (DBaaS) offering built on top of the popular MySQL relational database platform.
Traditionally, MySQL has focused on Online Transaction Processing (OLTP) workloads, but with Heatwave, it has been extended to support Online Analytical Processing (OLAP) as well. This expansion opens up new possibilities for users of MySQL, allowing them to perform faster queries and gain analytical insights from their data.
One of the major advancements introduced by Heatwave is the ability to query data stored in cloud object storage, commonly known as a data lake. This concept, known as the data lakehouse, aims to bridge the gap between traditional databases and data warehouse technologies. It combines the indexing and storage capabilities of a data warehouse with the cost-effectiveness and ease of use of a cloud data lake.
Oracle initially introduced the MySQL Heatwave Lakehouse service as a preview in October 2022 and has now made it generally available on Oracle Cloud Infrastructure (OCI) and Microsoft Azure. Oracle plans to extend availability to Amazon Web Services in the near future, ensuring that users can leverage the service regardless of where their data is located.
Nipun Agarwal, Oracle’s Senior Vice President of MySQL Database and MySQL HeatWave, highlighted the performance benefits of the service. He stated that whether the data is stored in the object store or the database itself, the performance remains identical, offering users flexibility in their data querying process.
MySQL Heatwave utilizes in-memory query acceleration to enhance analytics and data warehouse capabilities. This in-memory acceleration is crucial for enabling the lakehouse functionality, enabling users to seamlessly query data stored in object storage using standard MySQL SQL queries.
However, it is worth noting that MySQL Heatwave currently does not support popular open-source data lake table formats like Apache Iceberg. Oracle plans to add support for additional file formats based on customer demand and requirements.
An interesting aspect of MySQL Heatwave is its ability to combine data from both native storage and the data lake to execute queries. The seamless integration and transparency between the two sources provide users with greater flexibility and ease of use.
Oracle’s foray into the data lakehouse business aligns with the company’s ongoing efforts in the field of AI and generative AI. While MySQL Heatwave does not yet include specific generative AI functionality, Oracle’s AutoML capabilities enhance the database’s potential for machine learning (ML) training workflows. Oracle envisions incorporating large language models (LLMs) into its portfolio in the future.
In summary, Oracle’s MySQL Heatwave Lakehouse service brings new capabilities to the MySQL database, allowing users to perform faster queries and gain analytical insights. By enabling querying of data stored in a data lake, Oracle bridges the gap between traditional databases and data warehouse technologies. With its seamless integration and transparency, MySQL Heatwave offers flexibility and ease of use, regardless of where organizations store their data.
