AWS Database Blog
Category: Analytics
Migrate billions of records from an Oracle data warehouse to Amazon Redshift using AWS DMS
Customers are migrating to Amazon Redshift to modernize their data warehouse solution and help save on their licensing, support, operations, and maintenance costs. To migrate data from an on-premises data warehouse to Amazon Redshift, you can use services such as AWS Database Migration Service (AWS DMS), AWS Schema Conversion Tool (AWS SCT), Amazon Simple Storage […]
Implement vertical partitioning in Amazon DynamoDB using AWS Glue
In this post, we show you how to use AWS Glue to perform vertical partitioning of JSON documents when migrating document data from Amazon Simple Storage Service (Amazon S3) to Amazon DynamoDB. You can use this technique for other data sources, including relational and NoSQL databases. DynamoDB can store and retrieve any amount of data, […]
How CSC Generation powers product discovery with knowledge graphs using Amazon Neptune
This post is co-written with Bobber Cheng and Ronit Rudra from CSC Generation. CSC Generation is a company that focuses on acquiring overlooked stores and catalog-based retailers and transforming them into high-performance, digital-first brands. As we grew through multiple acquisitions, it became apparent that our legacy product information system (PIM), backed by relational databases, was […]
Writing results from an Athena query to Amazon DynamoDB
Many industries are taking advantage of the Internet of Things (IoT) to track information from and about connected devices. One example is the energy industry, which is using smart electricity meters to collect energy consumption from customers for analytics and control purposes. Vector, a New Zealand energy company, combines its energy knowledge with Amazon Web […]
Export and analyze Amazon QLDB journal data using AWS Glue and Amazon Athena
Amazon Quantum Ledger Database (Amazon QLDB) is a fully managed ledger database that maintains a complete, immutable record of every change committed to the database. As transactions are committed to the database, they are appended to a transaction log called a journal and are cryptographically hash-chained to the previous transaction. Once committed, the record of […]
Access Bitcoin and Ethereum open datasets for cross-chain analytics
In this post, we share an open-source solution for running cross-chain analytics on public blockchain data along with public datasets for Bitcoin and Ethereum available through AWS Open Data. These datasets are still experimental and are not recommended for production workloads. You can find the open-source project on GitHub here and the public blockchain datasets […]
Modernize legacy databases using event sourcing and CQRS with AWS DMS
When moving from monoliths to microservices, you often need to propagate the same data from the monolith into multiple downstream data stores. These include purpose-built databases serving microservices as part of a decomposition project, Amazon Simple Storage Service (Amazon S3) for hydrating a data lake, or as part of a long-running command query responsibility segregation […]
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 […]
Archive data from Amazon DynamoDB to Amazon S3 using TTL and Amazon Kinesis integration
August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. In this post, we share how you can use Amazon Kinesis integration and the Amazon DynamoDB Time to Live (TTL) feature to design data archiving. Archiving old […]
Combine Amazon Neptune and Amazon OpenSearch Service for geospatial queries
Many AWS customers are looking to solve their business problems by storing and integrating data across a combination of purpose-built databases. The reason for that is purpose-built databases provide innovative ways to build data access patterns that would be challenging or inefficient to solve otherwise. For example, we can model highly connected geospatial data as […]