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

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

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Compare side-by-side

Comparisons	AWS Data Pipeline	Glue Elastic Views
Category	Analytics, Data integration / ETL	Analytics, Data integration / ETL
Description	Process and move data between different AWS compute and storage services on a scheduled basis.	Build materialized views that automatically combine and replicate data across multiple data stores.
Best for	Scheduled data movement ETL workflows Cross-region data copy Data backups	Cross-store data replication Materialized views Data synchronization Real-time data access
Key features	Scheduling Retry logic Dependency tracking On-premises support Multiple data sources	SQL-based views Automatic replication Multiple target stores Change data capture Monitoring
Pricing model	Pay per activity and precondition	Pay per vCPU-hour for replication
Expert take	“AWS Data Pipeline orchestrates data movement between AWS services on a schedule. For new workloads, Step Functions or Glue workflows are more modern alternatives.” — Gary McLean, re:Post Top Contributor [profile]	“AWS 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 Data Pipeline »	Learn more about AWS Glue Elastic Views »

When to use AWS Data Pipeline or AWS Glue Elastic Views

Use AWS Data Pipeline when:

Scheduled data movement
ETL workflows
Cross-region data copy
Data backups

Learn more about AWS Data Pipeline »

Use AWS Glue Elastic Views when:

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

Learn more about AWS Glue Elastic Views »

Next steps with AWS for Analytics

Create a free account

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

Get started with AWS Data Pipeline

Process and move data between different AWS compute and storage services on a scheduled basis.

Go to AWS Data Pipeline

Get started with AWS Glue Elastic Views

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

Go to AWS 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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