5 reasons why MySQL HeatWave on OCI is better than Amazon Redshift
Here are the top five reasons to choose MySQL HeatWave on Oracle Cloud Infrastructure (OCI) over Amazon Redshift.
- Simplicity: MySQL HeatWave enables transactions, real-time analytics across data warehouses and data lakes, and machine learning in one cloud database service, without extract, transform, and load (ETL) across cloud services.
- Better performance: MySQL HeatWave is 4X faster than Amazon Redshift. The query performance of MySQL HeatWave Lakehouse is 9X faster than Amazon Redshift, and the load performance is also 9X faster. MySQL HeatWave AutoML trains models 25X faster than Redshift ML.
- Lower cost: MySQL HeatWave is one-fifth the cost of Amazon Redshift, and MySQL HeatWave AutoML is 1% of the cost of Redshift ML. MySQL HeatWave Lakehouse provides 8X better price-performance than Redshift for both query processing and data loading.
- Machine learning–powered automation: MySQL Autopilot provides workload-aware, machine learning–powered automation of various aspects of the application lifecycle, including provisioning, data loading, query execution, and failure handling.
- Increased data protection: MySQL HeatWave eliminates the risk of data movement between data stores and provides advanced security features to protect data throughout its lifecycle and support compliance with regulatory requirements.
"MySQL HeatWave dramatically reduced our AWS Aurora and Redshift cost by more than 50%. We are no longer moving data around, so now we have blazing fast, real-time insights with no effort. More importantly, scalability has made our expansion plan possible, allowing us to onboard more data and new clients without impact to costs. It's a dream come true."
Pablo Lemos
Cofounder and CTO, Tetris.co
1. Simplicity
Capability and evidence |
MySQL HeatWave |
Amazon Redshift |
|---|---|---|
One cloud database service for OLTP and OLAP across data warehouses and data lakes |
yes
Customers can run both OLTP and analytics workloads across data warehouses and data lakes in a single cloud database service—without changes to existing MySQL and Aurora-based applications. For mixed OLTP and OLAP workloads, applications access a single endpoint using a single SQL syntax. |
no
Amazon Redshift is a forked version of PostgreSQL without OLTP capabilities. Customers require a separate OLTP database. For mixed OLTP and OLAP workloads, applications must access two different endpoints using two different SQL syntaxes. |
No ETL duplication |
yes
The complex, time-consuming, and expensive ETL is eliminated. |
no
Single-purpose databases require an ETL process to move data between OLTP and OLAP services. While the "zero-ETL" integration of Amazon Aurora and Redshift simplifies the process, data is still replicated between two separate database services for OLTP and OLAP, creating complexity and generating costs (more details are in section 3). |
Real-time analytics |
yes
Queries always access the most up-to-date data; there’s no data transfer between databases. |
no
By the time data goes through ETL and is available in Redshift, it’s already stale. Even with zero-ETL integration between Aurora and Redshift, the latency of replicating data between two databases can be problematic for applications requiring real-time analytics. |
In-database machine learning |
yes
With HeatWave AutoML, developers and data analysts can build, train, deploy, and explain machine learning models within MySQL HeatWave. Data and ML models don’t leave the database, which speeds up results and prevents the risks of data movement between data stores. |
no
A separate ML service, such as Amazon SageMaker, is required—even when using Redshift ML. Data is copied to a separate location, and ML models are built outside Redshift. |
Automated machine learning lifecycle |
yes
The ML lifecycle is fully automated, including algorithm selection, intelligent data sampling, feature selection, and hyperparameter tuning for all model types. |
no
Redshift ML requires data science expertise to select the best algorithm and influence the performance, accuracy, and cost of training. |
Explainable data models and predictions |
yes
All ML models and predictions are explainable, increasing trust, fairness, causality, and repeatability and helping with regulatory compliance. |
no
Predictions from ML models in Redshift ML aren’t explainable, which may reduce trust, increase risks for bias, and could make regulatory compliance more difficult. |
2. Better performance
According to a 10 TB TPC-H benchmark, MySQL HeatWave on OCI is 4X faster than Amazon Redshift.
| Service | Average execution time in seconds (Geomean) |
|---|---|
| MySQL HeatWave (10 nodes) | 14.23 |
| Amazon Redshift (10 nodes of ra3.4xlarge) | 59 (4X slower) |
Capability and evidence |
MySQL HeatWave |
Amazon Redshift |
|---|---|---|
Real-time elasticity without downtime or read-only time |
yes
Customers can increase or decrease the size of their HeatWave cluster by any number of nodes without incurring any downtime or read-only time. Data is automatically rebalanced among all available cluster nodes for high performance. |
no
With elastic resize, the Redshift cluster is unavailable for four to eight minutes of the resize period. There are several limitations to consider, and the elastic resize of the Redshift cluster can cause data skew between nodes from an uneven distribution of data slices—which can severely downgrade the performance of queries. |
As demonstrated by a 500 TB TPC-H benchmark, the query performance of MySQL HeatWave Lakehouse is 9X faster than Amazon Redshift, and the load performance is also 9X faster.
| Service | Average execution time in seconds (Geomean) |
|---|---|
| MySQL HeatWave (512 nodes) | 47 |
| Amazon Redshift (20-ra3.16xlarge) | 423.3 (9X slower) |
| Service | Load time in hours |
|---|---|
| MySQL HeatWave (512 nodes) | 4.43 |
| Amazon Redshift (20-ra3.16xlarge) | 40.86 (9X slower) |
Benchmarks also demonstrate that, on average, HeatWave AutoML produces more accurate results than Amazon Redshift ML, trains models 25X faster, and scales as more nodes are added.
3. Lower cost
Compared to Amazon Redshift with discounted and upfront one-year reserved pricing, MySQL HeatWave is about one-fifth the cost and provides 23X better price-performance, as demonstrated by a 10 TB TPC-H benchmark.
| Service | Cost |
|---|---|
| MySQL HeatWave (10 nodes) | $34,073 |
| Amazon Redshift (10 nodes of ra3.4xlarge) | $188,480 (~5.5X more expensive) |
Note: This cost comparison doesn’t consider that with Amazon Redshift you need to pay for a separate OLTP database, such as Amazon Aurora, and for the data transfer between the two databases—which you can avoid with MySQL HeatWave. Hence, the savings can be greater with MySQL HeatWave.
MySQL HeatWave Lakehouse provides 8X better price-performance than Redshift for both query processing and data loading, as demonstrated by a 500 TB TPC-H benchmark.
Benchmarks also demonstrate that, on average, HeatWave AutoML is 1% of the cost of Amazon Redshift ML.
Amazon Web Services notes that while it doesn’t charge an additional fee for Aurora zero-ETL integration with Redshift, “you pay for existing Amazon Aurora and Amazon Redshift resources used to create and process the change data created as part of a zero-ETL integration. These resources may include additional I/O and storage used by enabling change data capture, Snapshot export costs for the initial data export to seed your Amazon Redshift databases, additional Amazon Redshift storage for storing replicated data, and cross-AZ data transfer costs for moving data from source to target.”
4. Machine learning–powered automation
Capability and evidence |
MySQL HeatWave |
Amazon Redshift |
|---|---|---|
Automated query performance tuning |
yes
MySQL Autopilot learns from the execution of queries and uses machine learning to automatically improve the performance of subsequent queries. |
no
Query plans aren’t automatically improved using machine learning models. |
Automated provisioning of the optimal cluster size |
yes
MySQL Autopilot uses machine learning to automatically provision the optimal cluster size for a given data set, whether the data resides in MySQL or in the object store. |
no
With Redshift, users need to answer questions to get a recommendation on the cluster size to provision. |
Automated data loading |
yes
MySQL Autopilot analyzes the data in the object store to predict the load time into the in-memory HeatWave cluster and automatically loads the data. |
no
Redshift doesn’t predict the load time into the cluster, making planning more difficult for DBAs. |
5. Increased data protection
Capability and evidence |
MySQL HeatWave |
Amazon Redshift |
|---|---|---|
No ETL process |
yes
The risk of data movement between data stores is eliminated. |
no
Data security and regulatory compliance risks can increase as data moves between separate services for OLTP, OLAP, and ML. Even with zero-ETL integration between Aurora and Redshift, data moves between two separate services. |
Digital signatures to confirm the authenticity and integrity of messages |
yes
Built-in server-side asymmetric encryption with key generation and digital signatures is provided. |
no
Built-in server-side asymmetric encryption to implement digital signatures isn’t provided. |
Built-in server-side data masking |
yes
Data masking and deidentification are built-in, helping protect the confidentiality of private data. |
no
Data masking and deidentification need to be implemented at the application level. |
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