What's the Difference Between AWS Glue DataBrew and Glue Elastic Views?

Compare AWS Glue DataBrew and Glue Elastic Views side by side — features, pricing, and ideal use cases to help you choose the right product.

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Comparisons	AWS Glue DataBrew	Glue Elastic Views
Category	Analytics, Data Preparation	Analytics, Data integration / ETL
Description	Visual data preparation tool for cleaning and normalizing data without writing code.	Build materialized views that automatically combine and replicate data across multiple data stores.
Best for	Data cleaning Data normalization Data profiling Feature engineering	Cross-store data replication Materialized views Data synchronization Real-time data access
Key features	250+ transformations Visual interface Data profiling Recipe jobs S3/Redshift/RDS sources	SQL-based views Automatic replication Multiple target stores Change data capture Monitoring
Pricing model	Pay per DataBrew session + node hours	Pay per vCPU-hour for replication
Free tier	Yes	—
Expert take	“Glue DataBrew provides a visual interface for data cleaning and normalization with 250+ built-in transformations. It generates profile reports that highlight data quality issues.” — Didier Durand, re:Post Top Contributor [profile]	“Glue Elastic Views replicates data across AWS data stores automatically. Define a view once and it materializes in DynamoDB, S3, Redshift, or OpenSearch without ETL pipelines.” — Didier Durand, re:Post Top Contributor [profile]
Product page	Learn more about AWS Glue DataBrew »	Learn more about Glue Elastic Views »

When to use AWS Glue DataBrew or Glue Elastic Views

Use AWS Glue DataBrew when:

Data cleaning
Data normalization
Data profiling
Feature engineering

Learn more about AWS Glue DataBrew »

Use AWS Glue Elastic Views when:

Cross-store data replication
Materialized views
Data synchronization
Real-time data access

Learn more about Glue Elastic Views »

Next steps with AWS for Analytics

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Start building with AWS Glue DataBrew and Glue Elastic Views on the AWS Free Tier.

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Visual data preparation tool for cleaning and normalizing data without writing code.

Go to AWS Glue DataBrew

Get started with Glue Elastic Views

Build materialized views that automatically combine and replicate data across multiple data stores.

Go to Glue Elastic Views

AWS service comparisons

What's the difference between Amazon Aurora and Amazon RDS?

Both are fully managed relational database services on AWS, but Aurora is a cloud-native engine built for higher performance and availability, while RDS gives you familiar open-source and commercial engines with less operational overhead than self-managed.

What's the difference between Amazon DynamoDB and Amazon RDS?

DynamoDB is a serverless NoSQL database for applications with well-defined access patterns that need consistent single-digit millisecond performance. RDS is a managed relational database for applications that need SQL, joins, and transactional integrity across multiple engines.

What's the difference between Amazon DynamoDB and Amazon Keyspaces?

Both are fully managed, serverless-capable NoSQL databases on AWS. DynamoDB is AWS-native with its own API and data model; Keyspaces is Apache Cassandra-compatible, letting you use existing Cassandra drivers, tools, and expertise.

What are the differences between Amazon DynamoDB and Amazon DocumentDB?

Both are fully managed NoSQL databases on AWS, but they serve different data models. DynamoDB is a key-value store optimized for known access patterns at massive scale; DocumentDB is a document database for flexible JSON schemas with MongoDB compatibility.

What's the difference between Amazon DocumentDB and Amazon Neptune?

Both handle semi-structured data, but they're optimized for different query patterns. DocumentDB stores and queries JSON documents; Neptune stores and traverses relationships between entities.

What's the difference between Amazon Aurora and Amazon DynamoDB?

Both are fully managed AWS database services, but they serve fundamentally different data models and access patterns. Aurora is a relational database for structured data with relationships; DynamoDB is a NoSQL key-value store built for single-digit millisecond performance at any scale.

What's the difference between Amazon RDS and Amazon Redshift?

Both run SQL queries, but they're optimized for fundamentally different workloads. RDS handles transactional operations (inserts, updates, lookups) for your application. Redshift handles analytical queries (aggregations, scans, joins across billions of rows) for your data team.

What's the difference between Amazon Aurora and Amazon Neptune?

Both are purpose-built, fully managed AWS databases, but they solve fundamentally different problems. Aurora handles structured relational data with SQL; Neptune handles highly connected data where relationships between entities are the primary query pattern.

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