Oracle today announced new HeatWave capabilities, including innovations to help organizations more easily and securely take advantage of generative AI both in Oracle Cloud Infrastructure (OCI) and in Amazon Web Services (AWS). Additional new capabilities help customers quickly and securely implement lakehouse and machine learning applications for a wider variety of use cases, as well as help improve the performance and manageability of transactional applications.

“Our track record of delivering powerful new HeatWave capabilities that are automated, integrated, and secure now extends to AI. Some organizations, such as SmarterD, are building new generative AI applications on HeatWave and moving into production in less than a month,” said Edward Screven, chief corporate architect, Oracle. “To enable more customers to take advantage of our innovations, including HeatWave GenAI with in-database LLMs, we are making them available natively on AWS. This enables AWS users to build rich generative AI applications without the need for AI expertise, without complex manual integrations and troubleshooting, and without the security risks and costs of moving their data to separate services.”

“The integration of generative AI in HeatWave is a major leap forward for us,” said Hans Ospina, CTO and founder, SOCOBOX. “By bringing in-database LLMs, automated vector processing, AutoML, and lakehouse into our workflows, we can now deliver powerful AI-driven insights and applications without the overhead of external tools. This comprehensive approach not only simplifies our operations but also ensures real-time, cost-effective solutions that resonate with the demands of our customers.”

Organizations applying analytics, transaction processing, machine learning, and generative AI across various data types and sources can leverage new HeatWave capabilities, including:

HeatWave on AWS

AWS customers can reduce complexity by replacing up to six AWS services with HeatWave. New HeatWave capabilities on AWS, which are also available on OCI, include:

• HeatWave GenAI: Helps AWS customers simplify the development of secure generative AI applications at a lower cost. Users can automate vector store creation and vector embedding generation, use in-database large language models (LLMs) running on CPUs or use models from Amazon Bedrock, and have natural language conversations with their documents in Amazon S3. A benchmark shows that vector processing with HeatWave offers 39X better price performance than Snowflake, 96X better than Databricks, and 48X better than Google Big Query. Vector creation with HeatWave is up to 30X faster and 1/3rd the cost of Knowledge Bases for Amazon Bedrock.
• HeatWave Lakehouse: Helps AWS users get faster insights from all their data by querying structured, semi-structured, and unstructured data in Amazon S3 with the industry’s best performance and price-performance.
• Native JavaScript support: Enables AWS users to write stored procedures and functions in JavaScript and execute them natively inside HeatWave, for example, to build dynamic content-loading applications using the rich features of JavaScript to process and query data in object storage.
• HeatWave Autopilot indexing: Enables AWS users to predict the optimal set of indexes needed for their OLTP workloads, reducing the time-consuming and complex task of determining the required indexes manually.

HeatWave GenAI

By providing integrated, automated, and secure generative AI, HeatWave GenAI lets developers build new generative AI applications without AI expertise, data movement, or additional cost. New capabilities include:

• Multi-lingual support: Helps developers build global applications with the ability to load documents in 27 languages into HeatWave Vector Store to perform similarity searches and ask questions in various languages.
• Optical Character Recognition (OCR) support: Helps users conduct similarity searches by leveraging HeatWave Vector Store to convert scanned content saved as images into text data that can be analyzed, for example, to detect plagiarism.
• LLM inference batch processing: Helps developers improve application throughput by executing multiple requests simultaneously across the HeatWave cluster.
• Automatic vector store update: Will help developers build applications using the latest data as changes to documents in object storage automatically trigger updates to corresponding vector embeddings.
• JavaScript support: Enables developers to use JavaScript with the VECTOR datatype and invoke HeatWave GenAI from a JavaScript program, for example, to more easily build chatbots accessing enterprise data.

HeatWave MySQL

HeatWave MySQL enables OLTP workloads to leverage the Enterprise Edition features of MySQL Database and delivers unique capabilities, such as Auto Shape Prediction, Auto Thread Pooling, Autopilot Indexing, and in-database JavaScript. New capabilities include:

• Hypergraph optimizer: Enables users to achieve true cost-based join optimization of query plans and improve performance, particularly for complex queries.
• Integration with OCI Ops Insights: Helps administrators uncover performance issues, forecast consumption, and plan capacity using ML-based analytics.
• Bulk ingest: Will enable users to load data into HeatWave MySQL up to 5X faster. As a result, data can be queried sooner, and resources are freed up faster, reducing costs.

HeatWave Lakehouse

HeatWave Lakehouse enables users to query hundreds of terabytes of data in object storage with the best price-performance in the industry. HeatWave Lakehouse can uniquely query data in object storage at the same speed as database queries without the need to copy the data from the object store to the database. New capabilities include:

• Writing results to object storage: Enables users to easily share query results and store them in object storage inexpensively. It also enables developers to use HeatWave for MapReduce applications.
• Automatic change propagation: Will enable users to always query the latest data by automatically detecting data changes in object storage and updating those changes incrementally to HeatWave.

HeatWave AutoML

HeatWave AutoML includes everything users need to build, train, and explain machine learning models within HeatWave at no additional cost. It provides support for classification, regression, anomaly detection, recommender systems, and time series forecasting. New capabilities include:

• Storing and processing larger models: Enables users to train ML models with a richer training data set by increasing capacity to accommodate 4X larger models.
• Topic modeling: Helps users discover insights in large textual data sets by helping them understand key themes in documents, for example, to complete sentiment analysis on social media data.
• Data drift: Helps users determine when to retrain models by detecting the differences between the data used for training and new incoming data.
• Semi-supervised log anomaly detection: Enables users to provide feedback on the results of unsupervised anomaly detection and use this labeled data to help improve subsequent predictions.

HeatWave Available in Oracle Cloud Free Tier

HeatWave is now available in the OCI Always Free Service, which enables organizations to develop and run small-scale applications using HeatWave MySQL, analytics, machine learning, JavaScript, HeatWave Vector Store, and process data in object store. All OCI accounts get access to a standalone HeatWave instance in OCI in their home region, along with 50 GB of storage and 50 GB of backup storage, for an unlimited time. They also receive $300 of credit to use for all eligible OCI services for up to 30 days.

Additional Resources

• Read the technical blog
• Read the Oracle HeatWave GenAI technical brief
• Learn more about Oracle HeatWave