5 reasons why HeatWave on OCI is better than Snowflake
Here are the top five reasons to choose HeatWave on Oracle Cloud Infrastructure (OCI) over Snowflake.
- Simplicity: HeatWave enables automated and integrated generative AI and machine learning (ML) in one cloud service for transactions and lakehouse-scale analytics. Customers can eliminate the complexity, latency, cost, and risks of ETL duplication across cloud services.
- Automated and integrated generative AI: HeatWave GenAI provides automated and integrated generative AI with in-database large language models (LLMs); an automated, in-database vector store; scale-out vector processing; and the ability to have contextual conversations in natural language—letting you take advantage of generative AI without AI expertise or data movement.
- Better performance and price performance: HeatWave MySQL is 4X faster than Snowflake, delivering 15X better price-performance. Heatwave Lakehouse is 18X faster than Snowflake, delivering 19X better price-performance.
- Machine learning–powered automation: HeatWave 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: 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.
"You can spend $80K on HeatWave and that would cost you $420K to run on Snowflake. It’s a no-brainer."
Patrick Moorhead
Founder and CEO, Moor Insights & Strategy
1. Simplicity
Capability and Evidence |
HeatWave |
Snowflake |
|---|---|---|
One cloud 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 service. |
no
Snowflake is designed only for analytics workloads. Customers can’t run rich and mature OLTP workloads directly on Snowflake. Snowflake’s Unistore is only in preview. |
No ETL duplication |
yes
The complex, time-consuming, and expensive ETL is eliminated. |
no
An ETL process is required to move data from OLTP sources to Snowflake. |
Real-time analytics |
yes
Queries 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 Snowflake, it’s already stale. |
In-database machine learning |
yes
With HeatWave AutoML, developers and data analysts can build, train, deploy, and explain machine learning models within 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
With Snowflake, users must rely on third-party machine learning tools or publicly available libraries to build, train, and deploy ML models. Snowflake doesn’t provide in-database machine learning. |
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
Snowflake doesn’t support automated, in-database machine learning. |
Explainable data models and predictions |
yes
All models and predictions are explainable, increasing trust, fairness, causality, and repeatability and helping with regulatory compliance. |
no
Snowflake doesn’t provide in-database machine learning with built-in explainability. |
2. Automated and integrated for generative AI
Capability and Evidence |
HeatWave |
Snowflake |
|---|---|---|
In-database LLMs |
Customers can use in-database, optimized LLMs across clouds and regions without the hassle of external LLM selection and integration. They can also choose external LLMs if needed. HeatWave helps customers significantly reduce infrastructure costs by eliminating the need to provision GPUs. | With Snowflake, users must rely on a separate service, Snowflake Cortex, to use external LLMs. This service has limited regional availability. External LLMs run on clusters of GPUs, increasing costs. |
Automated generation of vector embeddings |
HeatWave automates the generation of vector embeddings, including parsing, extracting metadata, creating chunks, and choosing embedding models for both data and queries—without requiring AI expertise. All steps are completed in-database without requiring data movement to separate client resources, simplifying the process and reducing costs. | Snowflake requires AI expertise and manual operations to create vector embeddings. Users first need to create chunks with user-defined functions that execute external third-party libraries using Snowflake’s proprietary development framework. Then they need to rely on Snowflake Cortex to create the vector embeddings. Using separate resources adds both complexity and cost. |
Accelerated vector processing |
Vector processing is parallelized across up to 512 HeatWave cluster nodes and executed at memory bandwidth, delivering extremely fast results. As demonstrated by a third-party benchmark, HeatWave GenAI is 30X faster than Snowflake. | Vector processing is executed on a proprietary development framework using third-party libraries. |
HeatWave Chat interface |
HeatWave Chat enables users to have natural language conversations informed by their unstructured documents. The context is preserved to allow follow-up questions. Developers don’t need to build a separate chat user interface, which accelerates the development cycle of GenAI apps. | Users need to build a custom chatbot using both Snowflake’s proprietary application framework and Streamlit. Using several application frameworks increases both compute and operational costs while slowing the development cycle of GenAI apps. |
3. Better query performance and price-performance
HeatWave MySQL is 4X faster than Snowflake, delivering 15X better price-performance, as demonstrated by a 10 TB TPC-H benchmark
| Service | Total query time in seconds |
|---|---|
| HeatWave MySQL (10 nodes) | 431 |
| Snowflake (X-Large) | 1800 |
| Service | Price-performance |
|---|---|
| HeatWave MySQL (10 nodes) | 1.077 |
| Snowflake (X-Large) | 10.371 |
Note: Savings can be greater with HeatWave MySQL since this comparison doesn’t consider that with Snowflake you need to pay for a separate OLTP database, such as Amazon Aurora, and for the data transfer between the two databases—you can avoid that with HeatWave MySQL.
As demonstrated by a 500 TB TPC-H benchmark, the query performance of HeatWave Lakehouse is 18X faster than Snowflake, delivering 19X better price-performance. The load performance of HeatWave Lakehouse is 2X faster than Snowflake.
| Service | Total query time in seconds |
|---|---|
| HeatWave Lakehouse (512 nodes) | 47 |
| Snowflake (4X-Large cluster) | 821 |
| Service | Price-performance |
|---|---|
| HeatWave Lakehouse (512 nodes) | 1.077 |
| Snowflake (4X-Large cluster) | 16 |
Capability and Evidence |
HeatWave |
Snowflake |
|---|---|---|
Real-time elasticity to any number of nodes |
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
Snowflake provides compute resources only in blocks of 1, 2, 4, 8, 16, 32, 64, 128, 256, and 512 nodes. Customers have no option but to overprovision their deployment by choosing a larger size than needed, spending more money than necessary. For example, scaling up from 32 nodes requires jumping to 64 nodes, even though only a small increment of compute resources may be needed. |
4. Machine learning–powered automation
Capability and Evidence |
HeatWave |
Snowflake |
|---|---|---|
Automated provisioning of the optimal cluster size |
yes
HeatWave 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
Developers and DBAs must guess or manually estimate by trial and error the optimal size of the cluster. |
Automated query performance tuning |
yes
Heatwave 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 schema inference |
yes
Heatwave Autopilot automatically infers the mapping of the file data to data types in the database, including for CSV file formats, by intelligently sampling portions of files in the object store. |
no
Snowflake can’t infer the mapping of the file data to data types in the database for CSV files. |
Automated data loading |
yes
Heatwave 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
Snowflake doesn’t provide a data load time capability. |
5. Increased data protection
Capability and Evidence |
HeatWave |
Snowflake |
|---|---|---|
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. |
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. |
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