AWS Database Blog

Category: AWS Glue

Migrate from Azure Cosmos DB to Amazon DynamoDB using AWS Glue

To take advantage of the performance, security, and scale of Amazon DynamoDB, customers want to migrate their data from their existing NoSQL databases in a way that is cost-optimized and performant. In this post, we show you how to migrate data from Azure Cosmos DB to Amazon DynamoDB through an offline migration approach using AWS […]

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Export and analyze Amazon DynamoDB data in an Amazon S3 data lake in Apache Parquet format

Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It’s a fully managed, multi-region, multi-active, durable database with built-in security, backup and restore, and in-memory caching for internet-scale applications. DynamoDB can handle more than 10 trillion requests per day and can support peaks of more than 20 million […]

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The following diagram illustrates this architecture.

Cross-account replication with Amazon DynamoDB

Update: For loading data into new DynamoDB tables, use the Import from S3 feature (announced on August 2022). Hundreds of thousands of customers use Amazon DynamoDB for mission-critical workloads. In some situations, you may want to migrate your DynamoDB tables into a different AWS account, for example, in the eventuality of a company being acquired […]

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Simplify Amazon DynamoDB data extraction and analysis by using AWS Glue and Amazon Athena

More than 100,000 AWS customers have chosen Amazon DynamoDB for mobile, web, gaming, ad tech, IoT, and many other applications. For example, Duolingo uses DynamoDB to store 31 billion items in tables that reach 24,000 read capacity units per second and 3,300 write capacity units per second. DynamoDB can address a wide variety of applications […]

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How to extract, transform, and load data for analytic processing using AWS Glue (Part 2)

  One of the biggest challenges enterprises face is setting up and maintaining a reliable extract, transform, and load (ETL) process to extract value and insight from data. Traditional ETL tools are complex to use, and can take months to implement, test, and deploy. After the ETL jobs are built, maintaining them can be painful […]

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