MySQL HeatWave Lakehouse can load 400 TB data from object storage 8X faster than Redshift and 2.7X faster than Snowflake
MySQL HeatWave Lakehouse scales to 512 nodes, can process hundreds of terabytes of data in object store in multiple file formats—including Aurora and Redshift backupsOracle CloudWorld, Las Vegas—October 18, 2022
Oracle today announced MySQL HeatWave Lakehouse, enabling 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 backups. MySQL HeatWave Lakehouse is the newest addition to the MySQL HeatWave portfolio, the only cloud service that combines transaction processing, analytics, machine learning, and machine learning-based automation within a single MySQL database.
Powered by the massively parallel scale-out MySQL HeatWave architecture, MySQL HeatWave Lakehouse delivers significantly better performance than competitive cloud database services for running queries and loading data, as demonstrated by industry standard benchmarks. 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. Oracle also announced new MySQL Autopilot capabilities that improve performance and make MySQL HeatWave Lakehouse easy to use. MySQL HeatWave Lakehouse is now available in Beta for customers to try and is slated for general availability in 1HCY23.
Customers migrating from AWS, Google, and on-premises have been using MySQL HeatWave for a broad set of use cases including marketing analytics, particularly real-time analysis of advertising campaign performance and customer data analytics to build effective campaigns. Customers migrating from AWS include leaders in the automotive, telecommunications, retail, high-tech, and healthcare industries.
“MySQL HeatWave is the result of years of research and advanced development, which we are turning into breakthrough innovations to address a bigger set of challenges for all MySQL customers. In fact, MySQL HeatWave Lakehouse is our third major MySQL HeatWave announcement this year,” said Edward Screven, chief corporate architect, Oracle. “There is a huge growth in data stored outside of databases, and with MySQL HeatWave Lakehouse, customers can leverage all the benefits of HeatWave on data residing in object store. MySQL HeatWave now provides one integrated service on multiple clouds for transaction processing, analytics across data warehouses and data lakes, and machine learning without ETL. This combination helps deliver massive improvements in performance, automation, and cost—further distancing MySQL HeatWave from other cloud database services.”
“We are excited to continue our collaboration with Oracle, evolving it into supporting their new MySQL HeatWave Lakehouse offering, which is optimized to run on AMD EPYC-powered Oracle cloud instances and leverage the latest innovations in our processors,” said Mark Papermaster, chief technology officer and executive vice president at AMD. “The collective work of the AMD and Oracle engineering teams has helped create an impressive MySQL solution that can support great scalability and performance for transaction processing, analytics, machine learning, and machine learning-based automation within a single MySQL database.”
Oracle is also publishing new lakehouse benchmarks and introducing several innovative capabilities for MySQL HeatWave Lakehouse and MySQL Autopilot.
“MySQL HeatWave Lakehouse sets the competition on fire by blazing the trail to the previously uncharted territory of 400 TB cloud database benchmarks at breakneck speeds,” said Ron Westfall, senior analyst and research director, Futurum Research. “MySQL HeatWave Lakehouse is a quantum leap for HeatWave in terms of processing capacity and computing power: from 32TB and 64 nodes to 400 TB and 512 nodes with performance and price performance that handily beat Amazon Redshift and Snowflake. Meanwhile, the cloud database competitors have yet to respond to the in-database convergence and the multi-cloud presence of MySQL HeatWave. How will they cope with the 400 TB MySQL HeatWave Lakehouse?”
MySQL Autopilot provides machine learning-based automation for MySQL HeatWave. Existing MySQL Autopilot capabilities such as auto provisioning and auto query plan improvement have been enhanced for MySQL HeatWave Lakehouse, which further reduces database administration overhead and improve performance. In addition, a number of new MySQL Autopilot capabilities are now available for MySQL HeatWave Lakehouse.
Oracle announced a number of other enhancements to MySQL HeatWave spanning from machine learning to the VS code plug-in. The in-database machine learning capabilities of MySQL HeatWave have been further enriched to include support for forecasting models. New machine learning explanation techniques have been added which have been optimized for MySQL HeatWave. Data scientists can now influence various stages of the automated HeatWave ML training pipeline, including the choice of algorithm, feature selection, scoring metric, and the explanation technique. HeatWave ML has also been enhanced to allow customers to import machine learning models into HeatWave.
A new multi-engine Hypergraph query optimizer further improves the performance of complex queries and eliminates the need to specify the join order. Zone map has been added, which accelerates a broader set of queries with MySQL HeatWave. And the VS code plug-in for MySQL has been enhanced to support MySQL HeatWave capabilities.
MySQL HeatWave is available in multiple clouds including OCI, AWS, and now Microsoft Azure. It’s available on-premises as part of OCI Dedicated Region for organizations that prefer not to move their database workloads to the public cloud. Customers can also replicate data from their on-premises MySQL OLTP applications to MySQL HeatWave to obtain near real-time analytics. MySQL HeatWave is always on the latest version of the MySQL database.
* Benchmark queries are derived from the TPC benchmarks, but results are not comparable to published TPC benchmarks results since these do not comply with the TPC specifications.
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