Tuesday, March 29, 2022 | Original broadcast
Oracle MySQL HeatWave is the only MySQL cloud database service with a built-in, high-performance, in-memory query accelerator—eliminating the need for complex, time-consuming, expensive ETL and integration with a separate analytics database.
Continuing to innovate on MySQL, Oracle introduces HeatWave ML—native, in-database machine learning (ML) capabilities that eliminate the need to move data to a separate machine learning tool and automate model training, generate inferences, and provide prediction explanations. It’s now easier, faster, more accurate, more secure, and more cost-effective for developers to incorporate machine learning into MySQL applications.
Watch the announcement and learn about all the latest enhancements—including real-time elasticity and data compression—to take the most advantage of MySQL HeatWave capabilities.
Edward Screven, Chief Corporate Architect, Oracle
Nipun Agarwal, Senior Vice President, MySQL HeatWave Development, Oracle
Vitor Pereira de Freitas, Cofounder and Chief Technology Officer, Estuda.com
Watch the top moments from Oracle Live.
Oracle Senior Vice President Nipun Agarwal demonstrates the new, real-time elasticity capabilities in Oracle MySQL HeatWave. See how you can scale up or down to any number of nodes without any downtime, ending the operation with a completely balanced cluster without any manual intervention.
In this presentation, Vitor Freitas, Estuda.com's Cofounder and CTO explains how this K-12 ed-tech in Brazil achieves real-time analytics with 300X faster complex query execution at 85% lower cost than Google BigQuery while supporting three million users.
This K-12 educational SaaS provider in Brazil improves complex query responses by 300X and at 85% less cost than Google BigQuery on a scale of three million users—all to enhance student performance.
The Brazilian metaverse startup migrated all its data to MySQL HeatWave from AWS EC2. Within 3 hours, it achieved 5X better database performance for an event with more than one million visitors with greater security and at the half the cost.
This fintech startup from Saudi Arabia moved its database workloads to MySQL HeatWave for 3X greater performance and 60% lower costs for greater efficiency than another cloud provider. Tamara has grown its client base to more than two million users and onboarded 3,000 unique merchants.
This Japanese video game company gained real-time insights by adding MySQL HeatWave to MySQL Database Service, helping it meet its goal of continuously improving joyful entertainment for customers around the world.
The ed-tech company leverages MySQL HeatWave for 300X faster analytics, 85% lower costs, and on-demand scalability.
A Japanese video game designer gains real-time insights and reduced costs with MySQL HeatWave for rapid application development.
Middle East and North African fintech moved to MySQL HeatWave, reducing costs more than 60% and tripling performance for many queries.
The Brazilian SaaS startup grew revenue by 3X and cuts costs in half with MySQL HeatWave for hosting metaverse events that scale to over 1 million visitors.
“We recently had an opportunity to use the machine learning capabilities of HeatWave ML. We found it very innovative, easy-to-use, very fast and most important it is secure since the data or the model don’t leave the database.”
“With the addition of HeatWave ML demonstrating up to 25X the speed of Redshift ML at 1% of the price, the MySQL HeatWave cloud service is not just a game changer, but represents a whole new engineering trajectory for cloud databases going forward.”
“For developers, HeatWave ML is a time saver, accelerating their app development velocity, increasing model accuracy.”
“Oracle’s HeatWave ML democratizes machine learning by automating the machine learning lifecycle—model training, inference, and explanations—and brings ML into HeatWave, no ETL required... As far as I’m aware, no other cloud provider goes this far with their ML when it comes to automation, explainability, or in providing ML benchmarks using standard data sets.”