Published on Oct 21, 2022
Designed to rapidly load and query up to 400TB of data, the new lakehouse service will compete with offerings from Oracle competitors such as Snowflake, Google, AWS, and Microsoft Azure.
Oracle on Tuesday launched its MySQL HeatWave Lakehouse service as part of its efforts to compete with its cloud-services rivals and help enterprises make better use of their accumulated data.
The MySQL HeatWave Lakehouse, announced at the Oracle CloudWorld conference, is currently in beta and is expected to become generally available in the first half of 2023. Designed to load and query up to 400 TB of data, Oracle’s HeatWave cluster can scale up to 512 nodes.
In essence, a data lakehouse is an architecture that combines the benefits of a data warehouse-such as structured data management and processing capabilities, including table formats, metadata management, and transactional updates and deletions-with the agility and low cost of a data lake.
Matt Aslett, Ventana Research’s research vice president, said the lakehouse architecture concept has gained popularity among enterprises that have invested in data lakes.
According to Aslett, by 2024, more than three-quarters of current data lake adopters will be investing in data lakehouse technologies.
The data lakehouse concept has been introduced by Oracle rivals including Snowflake, Databricks, Teradata, Dremio, Google, Amazon Web Services, and Microsoft Azure.
According to a Ventana report, data lakes have become an integral part of many organizations’ analytics data estates.
As vendors have begun offering cloud object storage as the underlying data repository, data lakes have gained prominence, providing an inexpensive means of storing large volumes of data from multiple enterprise applications and workloads. The importance of this is even greater for semistructured and unstructured data that cannot be stored and processed in a data warehouse.
According to Ventana Research’s Analytics & Data Benchmark Research poll, more than half (53%) of respondents are using object storage in their analytics efforts, with another 29% evaluating or planning to do so.
