Oracle MySQL HeatWave Lakehouse: Delivering a New Competitive Level Set for Cloud Data Warehouse

Oracle MySQL HeatWave Lakehouse Delivering a New Competitive Level Set for Cloud Data Warehouse

The News: Oracle announced the MySQL HeatWave Lakehouse, designed to enable customers to query data in object storage as fast as querying inside the database. Read the full Press Release on the Oracle website.

Oracle MySQL HeatWave Lakehouse: Delivering a New Competitive Level Set for Cloud Data Warehouse

Analyst Take: Oracle unveiled the general availability of MySQL HeatWave Lakehouse with immediate support for a variety of object store file formats such as CSV, Parquet, and export files from other databases, and can combine object storage file data and MySQL database transactional data together in the same query.

Oracle is fulfilling the market-wide demand for the Lakehouse feature in MySQL HeatWave by addressing the vast growth in data now stored in object store and data lakes. I find there is growing demand to securely analyze such data, however the immense size of the data, combined with the lack of structure and availability of standards-based query tools, make this a daunting and costly task. Also, many users prefer to avoid loading data in files in object store into databases to conduct analysis, due to expense, time, and complexity considerations. However, they are interested in combining data in a data lake with transactional data in databases to perform analytics.

Now object store files are queried directly by HeatWave without copying the data into the MySQL database. As such, MySQL HeatWave Lakehouse provides a new level set for scalability and performance for query processing, speed of loading data, cluster provisioning time, and automation to query data in object storage.

The new MySQL HeatWave Lakehouse offering spotlights the ability of the platform to query both object store and MySQL database as data stays in object store and is processed by HeatWave including across OLTP, analytics, Autopilot, ML, and Lakehouse applications. As such, customers are positioned to flexibly lock-in the rapid scale out capabilities of HeatWave.

Additionally, MySQL HeatWave Lakehouse can scale to any size up to 512 nodes with the added benefit of being able to elastically scale up or down according to workload demands. The system is always available for all operations and the data in the cluster is always balanced assuring real-time scaling. Both query performance and load performance scales very effectively with cluster size underlining the system’s high scalability capabilities.

MySQL HeatWave Lakehouse: Taking Data Lakehouse Price Performance to New Heights

Of key importance, a 10 TB TPC-H benchmark demonstrates that querying data in object storage in file formats with MySQL HeatWave Lakehouse is as swift as querying data in the MySQL database. Through MySQL Autopilot, MySQL HeatWave Lakehouse secures the ML-powered automation integral to learning from the execution of queries and improving the execution plan of future queries.

This includes MySQL Autopilot’s support for auto-schema inference, adaptive data sampling, and adaptive data flow capabilities. For example, adaptive data flow coordinates network bandwidth usage to the object store across a large cluster of nodes, dynamically adapting to the performance of the underlying object store, which can result in optimized performance and availability. From my view, only MySQL HeatWave can use MySQL Autopilot, which provides a sharp differentiator for the MySQL HeatWave Lakehouse proposition.

I see Oracle consistently using key competitive metrics, including published performance benchmarks available to all, that demonstrate the competitive advantages of the MySQL HeatWave portfolio. MySQL HeatWave Lakehouse is no exception. Oracle’s lakehouse system benchmarks address the top priorities of the key decision makers including performance price of loading data and querying data, comparing performance by querying data in a database, loading and querying performance for different file formats. Here are key performance metrics for querying data and loading data:

Querying Data Metrics

Querying Data Metrics
Image Source: Oracle

Oracle HeatWave Lakehouse delivers the performance advantages needed to move the market needle in its direction. For total query time, HeatWave Lakehouse only requires 2,150 seconds, while Snowflake needs 39,040 seconds (18x slower), Redshift needs 32,715 seconds (15x slower), Databricks needs 37,729 seconds (17x slower), and Google BigQuery needs 76,180 seconds (35x slower).

Also, for geomean query time, HeatWave Lakehouse only takes 47 seconds while Snowflake uses 821 seconds (17x slower), Redshift uses 423 seconds (9x slower), Databricks uses 788 seconds (17x slower), and Google BigQuery uses 1,713 seconds (35x slower). In my view, such performance advantages provide swift market-wide credibility to Oracle’s competitive positioning and market vision.

Loading Data Metrics

Loading Data Metrics
Image Source: Oracle

The performance to load data from the object store with MySQL HeatWave Lakehouse advantages consist of:

  • 9.2x faster than Redshift
  • 2x faster than Snowflake
  • 5.7x faster than Databricks
  • 8.6x faster than Google BigQuery

Moreover, MySQL HeatWave Lakehouse’s competitive advantages extend to key price performance evaluation and selection criteria. For example, the performance and price performance of querying object store data is identical to querying data from the database.

Data inside MySQL
Image Source: Oracle

From my view, this demonstrates that Oracle MySQL HeatWave Lakehouse delivers competitively advantageous price performance against key rivals for data warehouse workloads. Moreover, I discern that MySQL HeatWave is the only cloud database service that combines transactions, analytics, automatic ML, and now querying object store into one MySQL database, delivering real-time, secure analytics without the cost and burdens of extract, transform, and load (ETL) duplication.

Of topmost importance, data from object store is transferred into HeatWave in-memory representation during loading. This format is the same regardless of the source file format. As a result, the query performance is the same for all file formats in the object store. The performance of loading data is virtually always the same.

Key Takeaways: Oracle MySQL HeatWave Lakehouse

Overall, I believe MySQL HeatWave has consistently delivered industry-leading performance. The latest release of MySQL HeatWave Lakehouse, which delivers record performance for loading data from object store, is massively impressive. Databases either load data fast and defer some of the transformations to later—or databases do transformations at load time and are efficient at query time. For HeatWave Lakehouse to deliver record performance for both loading data and querying data is a breakthrough innovation across the entire cloud data warehouse market.

Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.

Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.

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Author Information

Ron is an experienced, customer-focused research expert and analyst, with over 20 years of experience in the digital and IT transformation markets, working with businesses to drive consistent revenue and sales growth.

He is a recognized authority at tracking the evolution of and identifying the key disruptive trends within the service enablement ecosystem, including a wide range of topics across software and services, infrastructure, 5G communications, Internet of Things (IoT), Artificial Intelligence (AI), analytics, security, cloud computing, revenue management, and regulatory issues.

Prior to his work with The Futurum Group, Ron worked with GlobalData Technology creating syndicated and custom research across a wide variety of technical fields. His work with Current Analysis focused on the broadband and service provider infrastructure markets.

Ron holds a Master of Arts in Public Policy from University of Nevada — Las Vegas and a Bachelor of Arts in political science/government from William and Mary.


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