MySQL HeatWave: Real-time Analytics without ETL
Deliver Real-time Analytics
HeatWave is designed to enable customers to run analytics on data which is stored in MySQL databases, without the need for ETL. Analytics queries always access the most up-to-date data as updates from transactions automatically replicate in real time to the HeatWave analytics cluster. There’s no need to index the data before running analytics queries.
In-Memory Query Acceleration
MySQL HeatWave is built on an innovative, in-memory analytics engine which is architected for scalability and performance. Most real-world applications have a mix of OLTP and complex OLAP queries. For such workloads, MySQL HeatWave is much faster and costs a fraction of Amazon Aurora. Using industry standard CH-benCHmark for OLAP queries on a 100GB dataset HeatWave is 18x faster, provides 110x better throughput and is 2.4x cheaper than Aurora for OLAP queries resulting in 42x better price performance.
Massively Parallel Architecture
One of the key design points of the HeatWave engine is to massively partition data across a cluster of HeatWave nodes, which can be operated upon in parallel in each node. Each HeatWave node within a cluster and each core within a node can process partitioned data in parallel, including parallel scans, joins, group-by, aggregation and top-k processing.
Hybrid Columnar Processing
Heatwave engine uses a hybrid columnar in-memory representation that facilitates vectorized processing, leading to very good query performance. The data is encoded and compressed prior to being loaded in memory. This results in significant performance speed up and reduced memory footprint which translates to reduced cost for customer.
Use Existing Business Intelligence (BI) and Data Visualization Tools
HeatWave supports the same BI and data visualization tools as MySQL Database, including Oracle Analytics Cloud, Tableau, and Looker.
Eliminate the Cost, Complexity, and Risk of ETL
MySQL HeatWave delivers the simplicity of transactions, real-time analytics, and machine learning in one database service. Organizations can eliminate the cost, complexity, latency and security risks of ETL processes required to move data between a separate analytics database and ML services.