HeatWave vs. Snowflake: Top Advantages
Oracle HeatWave is a fast, scalable, highly parallel data processing cloud service that lets businesses build lakehouse-scale data analytics and create generative AI applications from a single cloud service. HeatWave lets customers use their existing business intelligence tools and helps boost query performance through its in-memory query accelerator. Customers get real-time analysis of their transactional data without duplicating it via extract, transform, and load (ETL) to a separate analytics database.
For artificial intelligence–powered applications, users can take advantage of an automated, in-database vector store to perform retrieval-augmented generation (RAG), which lets AI answer queries using a company’s own data. Developers and analysts without AI experience can deploy large language models (LLMs) from Meta and Mistral that are included inside HeatWave for building AI applications. Compared with Snowflake’s data warehouse running on AWS, HeatWave on Oracle Cloud Infrastructure (OCI) ran queries derived from the TPC-H decision support benchmark in one quarter of the time on 10 TB of data (as of May 2023). Businesses can deploy HeatWave on OCI, AWS, and Microsoft’s Azure cloud.
Here are the top five reasons to choose HeatWave on OCI over Snowflake.
- Simplicity: HeatWave runs transaction processing, real-time analytics across data warehouses and data lakes, machine learning (ML), and generative AI in a single cloud database service, without needing to extract, transform, and load data across cloud services.
- Integrated, automated, and secure generative AI: HeatWave’s GenAI features provide large language models integrated into the database; an automated, in-database vector store; scale-out vector processing; and the ability to have contextual conversations in natural language, without the need for AI expertise.
- Higher performance and lower cost: HeatWave MySQL has been tested to perform four times faster than Snowflake on the 10 TB decision support benchmark, delivering 15 times better price-performance. Heatwave Lakehouse has been tested to perform 18 times faster than Snowflake, delivering 19 times better price-performance on a 500 TB TPC-H benchmark (as of June 2023).
- Machine learning–powered automation: HeatWave Autopilot helps improve performance and scalability by automating database tuning, including query plans using ML, partitioning, error handling, and compression.
- Increased data protection: HeatWave addresses the risk that moving data between stores can lead to security breaches and includes features that help protect data and support regulatory compliance.
“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
Simplicity and efficiency: HeatWave vs. Snowflake
HeatWave’s architecture lets businesses run OLTP and OLAP jobs in the same database service, reducing costly ETL work and letting businesses analyze fresh transaction data as it comes in.
Capability and evidence |
HeatWave |
Snowflake |
|---|---|---|
One cloud service for OLTP and OLAP that spans data warehouses and data lakes |
yes
Customers can run OLTP and analytics workloads across data warehouses and data lakes in a single cloud service, without changing existing applications. |
no
Snowflake is designed only for analytics workloads. Customers can’t directly run transaction-intensive OLTP workloads. Snowflake announced Unistore, for building transaction-processing applications, in 2022, but it’s still in public preview. |
No ETL duplication |
yes HeatWave reduces complex, time-consuming, expensive ETL. |
no
Customers need an ETL process to move data from OLTP databases to Snowflake. |
Real-time analytics |
yes
Queries access the most up-to-date information and there’s no data transfer between databases. |
no
By the time data goes through ETL and is available in Snowflake, it can already be outdated. |
Machine learning in the database |
yes
HeatWave’s AutoML features let developers and data analysts build, train, and explain ML models from within HeatWave. Data and models don’t need to leave the database, which helps speed up results and reduce security risks associated with data moving between stores. |
no
Snowflake users need to rely on third-party tools or publicly available libraries to build, train, and deploy machine learning models. Snowflake doesn’t provide ML in the database. |
Automated ML lifecycle |
yes
HeatWave fully automates the ML pipeline, including intelligent data sampling, feature selection, and hyperparameter tuning for all types of models. |
no Snowflake doesn’t support automated, in-database ML. |
Integrated, automated, and secure generative AI
Demand for developing generative AI applications enriched by businesses’ own data is high and HeatWave GenAI integrates many of the capabilities needed to build them. Developers can benefit from the automatic creation of vector embeddings needed for RAG; they provide company data that augments built-in LLMs from Meta and Mistral or external LLMs. And there’s no need to move data to a separate vector database. Native LLM execution within HeatWave helps reduce the risks associated with data movement. On a search benchmark looking for similar documents, HeatWave GenAI is 29 times faster than Snowflake and costs 25% less per hour (as of June 2024).
Capability and evidence |
HeatWave |
Snowflake |
|---|---|---|
In-database LLMs |
yes Large language models, including those from Meta and Mistral, are available directly in the database, without needing to select and integrate them. Customers can also choose external LLMs if desired. HeatWave users don’t need to provision GPUs for AI, helping reduce cloud infrastructure costs. |
no Snowflake users need to pay for a separate service called Cortex, which isn’t available in all global regions, to deploy LLMs. External LLMs run on GPU clusters, upping costs. |
Automated vector generation |
yes HeatWave automates generation of vector embeddings, including parsing, extracting metadata, splitting documents into “chunks” (to increase the relevance of AI systems’ answers), and choosing embedding models for data and queries— all without users needing AI expertise. All steps are completed in the database, helping simplify processes and reduce costs. |
no Snowflake requires AI expertise and much coding to create vector embeddings. Users need to create chunks with user-defined functions using Snowflake’s proprietary development framework. Then they need to rely on Snowflake’s Cortex service to create vector embeddings. That adds complexity and cost. |
Accelerated vector processing |
yes 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 29 times faster than Snowflake’s approach (as of June 2024). |
no Vector processing is executed on a proprietary development framework using third-party libraries. |
HeatWave Chat |
yes Users can have natural conversations informed by their organization’s private documents. Persistent context awareness allows for follow-up questions. Developers don’t need to build separate chat interfaces, accelerating generative AI development. |
no Users need to build custom chatbots using Snowflake’s proprietary application framework and Streamlit, a Python development tool. Using several application frameworks increases computing and operational complexity and costs, slowing generative AI app development. |
Cost efficiency: HeatWave vs. Snowflake
HeatWave runs the 10 TB TPC-H benchmark four times faster than Snowflake, with 15 times better price-performance. As demonstrated by a 500 TB TPC-H benchmark, the query performance of HeatWave Lakehouse is 18 times faster than Snowflake, delivering 19 times the price-performance. Loading data into HeatWave Lakehouse is twice as fast as with Snowflake.
| 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 Snowflake users also need to pay for a separate OLTP database, such as Amazon Aurora, and for data transfer between the two.
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 available cluster nodes for high performance. |
no
Snowflake provides computing resources only in building blocks of nodes, from one or two up to 512. Customers need to provision for peak capacity by choosing a larger size than needed, likely spending more than necessary. For example, scaling up from 32 nodes requires jumping to 64, even when less computing power may be needed. |
Intelligent automation: HeatWave’s machine learning advantage
HeatWave employs machine learning–powered automation to set up computing clusters, load data into memory, execute queries, and recover from software or hardware failures.
Capability and evidence |
HeatWave |
Snowflake |
|---|---|---|
Optimal cluster provisioning |
yes
HeatWave Autopilot uses machine learning to automatically provision optimal cluster sizes for given data sets, whether the data resides in MySQL or in an object store. |
no
Developers and DBAs need to guess the optimal cluster size or estimate by trial and error. |
Automated query performance tuning |
yes
HeatWave Autopilot learns from query execution and uses ML to automatically improve subsequent query performance. |
no
Query plans aren’t automatically improved via ML models. |
Automated database schema mapping |
yes
HeatWave automatically infers how to map CSV and other file formats to data types in the database, saving time and effort. |
no
Snowflake can’t infer the mapping of CSV and other formats to data types in the database. |
Automated data loading |
yes
HeatWave analyzes data in the object store to predict loading time into the in-memory cluster. Then it automatically loads the data. |
no
Snowflake doesn’t predict data loading times. |
Enhanced security: HeatWave’s superior data protection
HeatWave lets you query data in object storage, MySQL Database, or both, without ETL processes across cloud services. That means there’s less risk of data theft while it’s in transit.
Capability and evidence |
HeatWave |
Snowflake |
|---|---|---|
No ETL process |
yes
There’s no need for time consuming and error-prone ETL processes, reducing the risks associated with moving data between stores. |
no
Data security and regulatory compliance risks can increase as data moves between separate services for OLTP, OLAP, ML, and GenAI. |
Digital signatures |
yes
Built-in server-side, asymmetric encryption with key generation and digital signatures is provided to confirm data integrity. |
no
This capability isn’t provided. |
Final thoughts: Choosing between HeatWave and Snowflake
HeatWave vastly improves data analysis performance compared with Snowflake’s data warehouse and it lets businesses get immediate insights from their transactional data without the cost and risk of moving it into a separate database. HeatWave can run on Oracle’s cloud, AWS, and Azure and lets businesses build ML and generative AI applications from within the service—taking advantage of in-database LLMs and an automated in-database vector store. HeatWave is also more flexible than Snowflake, whose warehouse adds additional clusters as more users need to access the database, which can lead to higher pricing.
FAQs: HeatWave vs. Snowflake
What are the key differences between HeatWave and Snowflake?
HeatWave has much faster analytical query performance, wins on price-performance metrics, eliminates the need for ETL, and includes more generative AI and ML features out of the box.
How do HeatWave and Snowflake compare in terms of performance?
HeatWave has four times the query performance of Snowflake on a 10 TB data warehouse benchmark test and 18 times the performance on a large, 500 TB workload.
Which is more cost-effective: HeatWave or Snowflake?
HeatWave MySQL running on OCI delivers 15 times better price-performance than Snowflake on a 10 TB TPC-H benchmark and HeatWave Lakehouse provides 19 times better price-performance on the 500 TB test.
Try HeatWave for free.