AWS Glue Elastic Views makes it easy to build materialized views that combine and replicate data across multiple data stores without you having to write custom code.
New applications and features often require you to combine data that resides across multiple data stores, including relational and non-relational databases. Accessing, combining, replicating, and keeping this data up-to-date requires manual work and custom code that can take months of development time.
With AWS Glue Elastic Views, you can use familiar Structured Query Language (SQL) to quickly create a virtual table—a materialized view—from multiple different source data stores. AWS Glue Elastic Views copies data from each source data store and creates a replica in a target data store. AWS Glue Elastic Views continuously monitors for changes to data in your source data stores and provides updates to the materialized views in your target data stores automatically, ensuring data accessed through the materialized view is always up-to-date.
AWS Glue Elastic Views supports many AWS databases and data stores, including Amazon DynamoDB, Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service, with support for Amazon RDS, Amazon Aurora, and others to follow. AWS Glue Elastic Views is serverless and scales capacity up or down automatically based on demand, so there’s no infrastructure to manage. AWS Glue Elastic Views is available in preview today.
AWS Glue Elastic Views enables you to create materialized views across many databases and data stores using familiar SQL. AWS Glue Elastic Views supports Amazon DynamoDB, Amazon Redshift, Amazon S3, and Amazon Elasticsearch Service, with support for more data stores to follow.
AWS Glue Elastic Views handles all of the heavy lifting of copying and combining data from source to target data stores, without you having to write custom code or use unfamiliar ETL tools and programming languages. AWS Glue Elastic Views reduces the time it takes to combine and replicate data across data stores from months to minutes.
AWS Glue Elastic Views continuously monitors for changes to data in your source data stores, and when changes occur, Elastic Views automatically updates the target data store. This ensures that applications that access data using Elastic Views always have the most up-to-date data.
AWS Glue Elastic Views proactively alerts developers when there is a change to the data model in one of the source data stores so that they can update their view to adapt to this change.
AWS Glue Elastic Views is serverless and scales capacity up or down automatically to accommodate your workloads. There is no hardware or software to manage, and you pay only for what you use.
How it works
AWS Glue Elastic Views combines data from multiple data stores in near-real time, so you can support new applications and features with all required data in a single target data store. For example, you can combine website traffic data from an Amazon DynamoDB database with purchase history data from an Amazon Aurora database and copy it to a new Amazon DynamoDB database to support a marketing application that targets customers with promotions in real time based on browsing patterns.
AWS Glue Elastic Views replicates data across multiple data stores, so you can use the same data in the data store that is purpose-built for your use case. For example, you can create a copy of product catalog data in a DynamoDB table in Amazon Elasticsearch Service to enable full text search on the DynamoDB data.
AWS Glue Elastic Views simplifies running analytical queries on your most recent operational data. For example, you can create database views over data in your operational databases and materialize those views in your data warehouse or data lake.