HeatWave Lakehouse

HeatWave Lakehouse lets you query data in object storage with unmatched performance and price-performance—and automatically build, train, and explain machine learning (ML) models. It’s available on Oracle Cloud Infrastructure (OCI), Amazon Web Services (AWS), and Microsoft Azure.

Demo: HeatWave Lakehouse on AWS (4:32)

Watch the replay of Chief Corporate Architect Edward Screven’s Oracle CloudWorld keynote: “Build Generative AI Applications—Integrated and Automated with HeatWave GenAI.”

Why use HeatWave Lakehouse?

  • Query data in object storage

    HeatWave is a scale-out data processing engine optimized to query data in object storage. You can query structured data, semi-structured data in JSON format, and unstructured documents with HeatWave Vector Store.

  • Get the best performance and price-performance

    The query performance of HeatWave Lakehouse is 15X faster than Amazon Redshift, 18X faster than Snowflake, 18X faster than Databricks, and 35X faster than Google BigQuery, per a 500 TB TPC-H benchmark. Price-performance is also significantly better.

  • Use built-in ML with all your data

    Automate the pipeline to build, train, deploy, and explain ML models using data in object storage and MySQL Database, without moving the data to a separate ML cloud service and at no additional cost.

How HeatWave Lakehouse works

HeatWave Lakehouse processes data in a variety of file formats, including CSV, Parquet, Avro, JSON, and exports from other databases. You can query data in object storage and optionally combine it with transactional data in MySQL databases. With HeatWave Vector Store, you can upload and query unstructured documents. Data loaded into the HeatWave cluster for processing is automatically transformed into the HeatWave in-memory format, and object storage data isn’t copied to the MySQL database. You can also take advantage of HeatWave AutoML, a built-in feature that automates the pipeline to build, train, and explain ML models using data in object storage, the database, or both. There’s no need to move the data to a separate ML cloud service, and there’s no need to be an ML expert.

How Heatwave Lakehouse works diagram, details below:
Transactional data in MySQL Database as well as object storage data in a variety of file formats, such as CSV, Parquet, Avro, JSON, and exports from other databases is replicated in real-time into the HeatWave Cluster, enabling customers to get real-time analytics across all this data. Since machine learning capabilities are built in HeatWave, customers can also use data from the database and object storage to build, train, deploy, and explain machine learning models.

Customer perspectives on HeatWave Lakehouse

  • “HeatWave Lakehouse scales out very well for loading data from object storage and for running queries on object store. The load time and the query times are nearly constant as the size of the data grows and the HeatWave cluster size grows correspondingly. This scale out characteristic of HeatWave Lakehouse for data management is key to efficiently processing very large amounts of data.”

    —Henry Tullis, Leader, Cloud Infrastructure and Engineering, Deloitte Consulting

  • “Data is growing exponentially and so is the amount of data we store in our data lake. The ability to use standard MySQL syntax to query data across our database and object storage to get real-time insights is very important for Natura. This opens up new opportunities to explore and could represent new competitive advantages if we can analyze all this data faster than our competition.”

    —Fabricio Rucci, Solution Architect at Natura &Co

Use cases for HeatWave Lakehouse

Users can obtain rapid and cost-effective insights from historical transactions offloaded to object storage for analytics purposes using HeatWave Lakehouse.


Banking diagram, description below:

This diagram shows that it can be costly to retain all historical transactions data in transactional database on-premises for analytics. Older transactions data is therefore exported as CSV files to object storage. HeatWave Lakehouse enables fast queries on data in object storage, allowing users to get rapid and cost-effective insights from historical transactions data.



With HeatWave Lakehouse, users can obtain insights across recent campaign data in their transactional database and older campaign data in their data lake.


Digital marketing diagram, description below:

This diagram shows that all campaign data is stored in the transactional database HeatWave MySQL and older campaign data is exported to a data lake in object storage. HeatWave Lakehouse can query recent data in the database combined with older campaign data in object storage, enabling users to run analytics queries across all campaign data.



Data generated by Internet of Things (IoT) sensors can be accessed by applications via HeatWave Lakehouse.


IoT diagram, description below:

This diagram shows that data is generated from IoT sensors on shipping containers and is stored as CSV files in a data lake in object storage. HeatWave Lakehouse can rapidly query this data, enabling users to implement analytics dashboards and chatbots accessing IoT data.



Users can manage and plan media sales campaigns using HeatWave Lakehouse to simultaneously query sales data in their transactional database as well as sales statistics and campaign data in object storage.


Media diagram, description below:

This diagram shows that book sales are recorded and stored in the transactional database HeatWave MySQL. Statistics on sales and campaigns are gathered and that data is exported as CSV files to object storage. HeatWave Lakehouse can query transactional data combined with data in object storage, allowing users to manage and plan sales campaigns.



See what top industry analysts say about HeatWave Lakehouse

  • IDC logo

    “Organizations looking for the best value in the cloud data lakehouse landscape must seriously consider HeatWave Lakehouse.”

    Carl Olofson
    Research Vice President, Data Management Software, IDC
  • Wikibon logo

    “The ability of HeatWave to load and query data on such a massive number of nodes in parallel is the first in the industry.”

    Marc Staimer
    Senior Analyst, Wikibon
  • The Futurum Group logo

    “For HeatWave Lakehouse to deliver record performance for both loading data and querying data is an unprecedented innovation in cloud data services.”

    Ron Westfall
    Research Director, The Futurum Group
  • Omdia logo

    “With HeatWave Lakehouse, Oracle is presenting MySQL customers on AWS and Microsoft Azure with a proposition they may not be able to refuse.”

    Bradley Shimmin
    Chief Analyst, AI & Data Analytics, Omdia
  • Moor logo

    “HeatWave Lakehouse can simplify the life of data management professionals and should improve the customer experience.”

    Matt Kimball
    Vice President and Principal Analyst, Datacenter, Moor Insights & Strategy
  • NAND Research logo

    “Simply put: HeatWave Lakehouse enables you to stay ahead of the competition by taking swift action on meaningful business insights.”

    Steve McDowell
    Principal Analyst and Founding Partner, NAND Research
  • Constellation Research logo

    “The HeatWave team has out-innovated the industry, providing five different core database use cases in a single database, which gives CxOs the peace of mind to have one database that can do it all—a truly universal database.”

    Holger Mueller
    Vice President and Principal Analyst, Constellation Research
  • KuppingerCole logo

    “HeatWave Lakehouse takes customers to a new level of capabilities: being able to query heterogeneous data across data warehouses and data lakes at petabyte scale using the familiar SQL syntax, while beating popular competitors at query performance, load times, and cost efficiency.”

    Alexei Balaganski
    Lead Analyst & CTO, KuppingerCole Analysts

Learn more about other HeatWave solutions for your different workloads

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Learn with step-by-step guidance

Experience HeatWave Lakehouse at your own pace with step-by-step instructions.

  • Analyze data at scale in object storage with HeatWave Lakehouse

    Learn how to query hundreds of terabytes of data in various file formats in object storage using HeatWave Lakehouse. Discover how simple this process can be with HeatWave Autopilot.

    Start this lab about analyzing data at scale in object storage with HeatWave Lakehouse

  • Win the race with HeatWave Lakehouse

    This is your chance to see what it means to be a "data athlete" for SailGP by helping the sailing teams increase their performance. SailGP provides the perfect playground to try out HeatWave Lakehouse; the high-speed, high-tech boats contain more than 800 IoT sensors that generate more than 12,000 data points.

    Start this lab about winning the race with HeatWave Lakehouse

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