HeatWave on AWS vs. Amazon Aurora, Redshift, and Snowflake

Here are the top five reasons to choose Oracle HeatWave on Amazon Web Services (AWS) over Amazon Aurora and Amazon Redshift and over Amazon Aurora and Snowflake.

  1. Simplicity: HeatWave enables automated, integrated, and secure 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.
  2. Automated and integrated generative AI: HeatWave GenAI provides automated, integrated, and secure 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, data movement, or additional cost.
  3. Better price-performance: HeatWave MySQL provides up to 10X better throughput than Amazon Aurora, 7X better price-performance than Amazon Redshift, and 10X better price-performance than Snowflake on AWS.
  4. 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.
  5. 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.

1. Simplicity



Capability and evidence
HeatWave on AWS
Amazon Aurora and Redshift
Amazon Aurora and Snowflake
One database service for OLTP and OLAP workloads across data warehouses and data lakes on AWS
yes

Customers can run OLTP and and analytics workloads across data warehouses and data lakes in a single cloud service—without changes to current applications based on MySQL and Aurora. For mixed OLTP and OLAP workloads, applications access a single endpoint using a single SQL syntax.
no

Amazon Aurora is for OLTP; customers need a separate OLAP service, such as Redshift. For mixed OLTP and OLAP workloads, applications must access two different endpoints using two different SQL syntaxes.
no

Amazon Aurora is for OLTP; customers need a separate OLAP service, such as Snowflake. 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 Aurora and Redshift simplifies the process, data is still replicated between two separate database services for OLTP and OLAP, creating complexity and generating costs.
no

Single-purpose databases require an ETL process to move data between OLTP and OLAP services.
Real-time, secure 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. Moving data between stores can present additional security risks. Even with zero-ETL integration between Aurora and Redshift, data moves between stores, and the latency of replicating data between two databases can be problematic for applications requiring real-time analytics.
no

By the time data goes through ETL and is available in Snowflake, it’s already stale. Moving data between stores can present additional security risks.
In-database machine learning
yes

With HeatWave AutoML, developers and data analysts can build, train, and explain machine learning models within HeatWave.
no

A separate ML service, such as Amazon SageMaker, is required.
no

A separate ML service, such as Amazon SageMaker, is required.
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 Aurora ML and Redshift ML aren’t explainable, which may reduce trust and increase risks for bias and could make regulatory compliance more difficult.
no

Predictions from ML models in Aurora ML aren’t explainable, which may reduce trust and increase risks for bias and could make regulatory compliance more difficult. Snowflake requires a third-party ML service.
Automated machine learning lifecycle
yes

The ML lifecycle is fully automated, including algorithm selection, intelligent data sampling, feature selection, and hyper-parameter tuning.
no

Aurora ML and Redshift ML require data science expertise to influence the performance, accuracy, and cost of training.
no

Aurora ML and Snowflake ML require data science expertise to influence the performance, accuracy, and cost of training.

2. Automated and integrated generative AI



Capability and evidence
HeatWave on AWS
Amazon Aurora and Redshift
Amazon Aurora and Snowflake
In-database LLMs
yes

Customers can use in-database LLMs without the hassle of external LLM selection and integration. They can also choose external LLMs if needed. HeatWave helps customers reduce infrastructure costs by eliminating the need to provision GPUs.
no

AWS does not provide the ability to run LLMs inside Redshift or Aurora database engines. Customers require a separate service called Amazon Bedrock. Using multiple cloud services can increase complexity and costs and may also increase security risks.
no

With Amazon Aurora and Snowflake, users must rely on a separate service, either Amazon Bedrock or Snowflake Cortex, to use external LLMs. Snowflake Cortex has limited regional availability. External LLMs run on GPU clusters, which can increase costs for building GenAI applications.
Automated generation of vector embeddings
yes

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.
no

Amazon Aurora requires the pgvector extension, and vector processing is not automated. Amazon Redshift does not currently support in-database vector processing.
no

Amazon Aurora requires the pgvector extension, and vector processing is not automated. Snowflake requires AI expertise and manual operations to create vector embeddings. Users first need to create chunks with user-defined functions using Snowflake’s proprietary development framework. Then they need to rely on Snowflake Cortex to create the vector embeddings. This is a complex process and can increase costs.
Accelerated vector processing
yes

Vector processing is parallelized across up to 512 HeatWave cluster nodes and executed at memory bandwidth, helping deliver fast results at an improved speed. As demonstrated by a third-party benchmark, HeatWave GenAI is 29X faster than Snowflake.
no

Vector processing in Aurora is not unified in a single database instance and requires multiple services, making it difficult to optimize resources. Redshift does not currently support in-database vector processing.
no

Vector processing in Aurora is not unified in a single database instance and requires multiple services, making it difficult to optimize resources. In Snowflake, vector processing is executed on a proprietary development framework using third-party libraries, potentially making it expensive.
HeatWave Chat interface
yes

HeatWave Chat lets users have natural language conversations informed by their unstructured documents. The context is preserved to allow follow-up questions. Developers do not need to build a separate chat user interface, which helps accelerate the development cycle of GenAI apps.
no

AWS does not provide an out-of-the-box chat interface. Users need to use a separate service to build a chatbot, which may increase complexity and costs.
no

Aurora does not provide an out-of-the-box chat interface. In Snowflake, users need to build a custom chatbot using both Snowflake’s proprietary application framework and Streamlit. Using several application frameworks can increase both compute and operational costs while slowing the development cycle of GenAI apps.

3. Better price-performance

HeatWave MySQL on AWS delivers up to 10X better throughput than Amazon Aurora with HeatWave Autopilot, as demonstrated by a TPC-C benchmark.

TPC-C benchmark

HeatWave MySQL on AWS delivers 7X better price-performance than Amazon Redshift, as demonstrated by a TPC-H benchmark.

TPC-H benchmark

AWS notes that while it does not charge an additional fee for Aurora zero-ETL integration with Redshift, “you pay for existing Aurora and Amazon Redshift resources used to create and process the change data generated as part of a zero-ETL integration.” These resources, according to AWS, 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, additional Amazon Redshift compute for processing data replication, and cross-AZ data transfer costs for moving data from source to target.


HeatWave MySQL on AWS delivers 10X better price-performance than Snowflake on AWS, as demonstrated by a TPC-H benchmark.

TPC-H pricing benchmark

4. Machine learning–powered automation

HeatWave Autopilot automates many of the most important and often challenging aspects of achieving high query performance at scale.



Capability and evidence
HeatWave on AWS
Amazon Aurora and Redshift
Amazon Aurora and Snowflake
Automation using built-in machine learning
yes

HeatWave Autopilot automates provisioning, data loading, query execution, and failure handling—further improving performance while saving developers and DBAs significant time.
no

Built-in machine learning–powered automation isn’t available. Expertise in both databases and manual operations is required.
no

Built-in machine learning–powered automation isn’t available. Expertise in both databases and manual operations is required.
Automated workload-aware tuning for OLTP
yes

HeatWave Autopilot delivers high OLTP throughput that’s sustained at high levels of transactions and concurrency.
no

With Aurora, the throughput of the system drops at high levels of transactions and concurrency. Redshift can’t be used for OLTP.
no

With Aurora, the throughput of the system drops at high levels of transactions and concurrency. Snowflake can’t be used for OLTP.
Automated query performance tuning
yes

HeatWave Autopilot learns from the execution of queries to automatically improve the performance of subsequent queries.
no

Query plans are not automatically improved using machine learning models.
no

Query plans are not automatically improved using machine learning models.
Automated provisioning of the optimal cluster size
yes

HeatWave Autopilot autoprovisions the optimal cluster size for a given data set.
no

Developers and DBAs must guess or manually estimate by trial and error the optimal size of the cluster for both databases.
no

Developers and DBAs must guess or manually estimate by trial and error the optimal size of the cluster for both databases.

5. 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.



Capability and evidence
HeatWave on AWS
Amazon Aurora and Redshift
Amazon Aurora and Snowflake
Digital signatures 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 is not provided.
no
*
Built-in server-side asymmetric encryption to implement digital signatures is not 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.
no
*
For Aurora, data masking and deidentification need to be implemented at the application level. In Snowflake, data masking and deidentification features are available only in different editions of the database, which can make basic security functions cost more.
Built-in server-side database firewall
yes

A built-in server-side database firewall helps protect against various types of attacks, including some database-specific threats such as SQL injection.
no
*
A built-in server-side database firewall is not provided, which can leave the database vulnerable to ransomware attacks.
no
*
A built-in server-side database firewall is not provided, which can leave the database vulnerable to ransomware attacks.

* As of August 2024

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