April 18, 2024

Oracle’s MySQL HeatWave Lakehouse launches

It is no exaggeration to say that the speed with which a business can analyze business-critical data directly affects its ability to compete. Time is of the essence when it comes to mission-critical analytics.

However, one of the challenges of analyzing data is that not much of the enterprise’s data can be found in a database. You’d be surprised to see how much business-critical data lives outside the database. This is one reason why object storage is one of the fastest growing segments of the storage market, with a CAGR of 10-15% (depending on which analyst you believe). Object storage is where an organization’s work data resides.

Ideally, companies would get the most modern business insights from querying mission-critical operational data alongside CSV, IoT, and web files in object storage. The problem is that it’s complicated, time-consuming and expensive to transform file data before it’s loaded into the database for analysis – resulting in staff insights and or largely irrelevant data.

Oracle is providing a solution to this challenge with the release of its new MySQL HeatWave Lake House. Let’s look at what that is.

MySQL HeatWave Lake House

MySQL HeatWave, if you’re not familiar, is Oracle’s fully managed MySQL database service powered by the company’s unique HeatWave query accelerator technology. Heatwave is the only cloud service that combines transactions, real-time analytics across data warehouses and data lakes, and machine learning in a single MySQL Database. It does this without the complexity, patience, risks and cost of duplicating ETL.

Lake House extends Oracle’s MySQL HeatWave with MySQL HeatWave to meet the complexity of modern enterprise data. HeatWave Lakehouse enables users to query data stored outside the database, allowing you to analyze data stored in various file formats (CSV, Parquet, etc.) as if that data were stored directly within the MySQL database, all without penalty.

Users can query up to half a petabyte of non-database data, optionally combined with transactional data from a MySQL database, without copying data from the object store into the MySLQ database. This reduces the latency that would be introduced with the copy. HeatWave Lakehouse also uses a new unified query engine that allows data stored in object storage to be queried without a performance penalty.

The ability to query and run data in a content store greatly increases the speed and ease with which you can cost-effectively analyze data across both data warehouses and data lakes. MySQL HeatWave Lakehouse lets you stay ahead of the competition by quickly acting on meaningful business insights. This is a great ability.

Performance Leadership

MySQL HeatWave has been a performance leader since its introduction, following on from HeatWave Lakehouse. Oracle has shown that MySQL HeatWave Lakehouse is significantly faster than its closest competition on a benchmark derived from the industry standard TCP-H.

Oracle revealed that its new offering is 9x faster than Amazon Redshift, 17x faster than Databricks and Snowflake, and 36x faster than Google BigQuery.

Autopilot for HeatWave Lake House

MySQL Autopilot is Oracle’s solution for automating MySQL Heatwave. Autopilot uses machine learning and automation algorithms to optimize the performance of the MySQL HeatWave service. It monitors usage patterns and resource consumption of the MySQL HeatWave service and will automatically adjust the service configuration to maximize performance and reduce costs.

Oracle has enhanced Autopilot to support the unique needs of MySQL HeatWave Lakehouse. There are improvements to Autopilot’s provisioning and automatic query planning capabilities, along with changes to its auto parallel loading engine, to understand and leverage data stored in an object store.

In addition to changing MySQL AutoPilot to work with object stores, Oracle introduced new capabilities to Lakehouse. The new capabilities include:

· Automatic scheme inferenceswhich samples data from object storage to make inferences about the data within a CSV file.

· Adaptive data samplingwhich intelligently samples files to find information needed for automation.

· Adaptive data flow, which learns and coordinates network bandwidth usage to the object store across a cluster of nodes, dynamically adapting to the performance of the underlying object store. This creates the best performance and availability.

· Improved automatic query planwhich improves query planning by learning from previously executed queries.

Analytical Construction

It’s no secret that I’m a fan of MySQL HeatWave. It’s simply one of the best managed database offerings available from any cloud provider.

Oracle raises the bar even higher with MySQL Lakehouse, taking on the complexities of analyzing enterprise data outside the database, but as if that data were stored natively within the database. MySQL HeatWave Lakehouse does this without sacrificing performance. It offers the best load performance and query times in the industry. Throw in the MySQL Autopilot enhancements to automate data management; the story is unparalleled.

Oracle’s MySQL HeatWave team is on a mission to simplify enterprise data-centric workflows, operating at breakneck speed. Earlier this year, Oracle allowed users to run AI models directly in the database. Now Lakehouse’s MySQL HeatWave goes further, with Lakehouse allowing enterprises to tackle the complexity of mission-critical analytics, regardless of the location of the data in question. That’s a powerful story.

Disclosure: Steve McDowell is an industry analyst, and NAND Research, an industry analyst firm, engages in research, analysis and advisory services, or is involved in research, analysis and advisory services with many technology companies, which may include those mentioned in this article. Mr. McDowell has no equity position with any company mentioned in this article.

Leave a Reply

Your email address will not be published. Required fields are marked *