AWS Database Blog

For very large migrations, AWS Database Migration Service (AWS DMS) replication can run for hours or days depending on the data being replicated. It’s advisable to monitor the AWS DMS resources for a smooth migration. Monitoring your resources can help you detect anomalies and trigger notifications based on the threshold metrics configured. You can use […]

This post discusses how these telecommunication companies used Amazon Managed Blockchain to build a universal mobile identity card platform and how this service works.

In this post, we demonstrate how to use Amazon SageMaker notebooks to connect to Amazon DocumentDB for a simple, powerful, and flexible development experience. We walk through the steps using the AWS Management Console, but also include an AWS CloudFormation template to add an Amazon SageMaker notebook to your existing Amazon DocumentDB environment.

To migrate an Oracle database to Amazon Aurora with PostgreSQL compatibility, you usually need to perform both automated and manual tasks. The automated tasks involve schema conversion using AWS Schema Conversion Tool (AWS SCT) and data migration using AWS Database Migration Service (AWS DMS). The manual tasks involve post-AWS SCT migration touch-ups for certain database […]

Amazon Neptune engine version 1.0.4.0 supports Apache TinkerPop 3.4.8, which introduces some new features and bug fixes. This post outlines these features, like the new elementMap() step and the improved behavior for working with map instances, and provides some examples to demonstrate their capabilities with Neptune. Upgrading your drivers to 3.4.8 should be straightforward and typically require no changes to your Gremlin code.

If your data is stored in existing relational databases, converting relational data structures to documents can be complex and involve constructing and managing custom extract, transform, and load (ETL) pipelines. Amazon Database Migration Service (AWS DMS) can manage the migration process efficiently and repeatably. With AWS DMS, you can perform minimal downtime migrations, and can replicate ongoing changes to keep sources and targets in sync. This post provides an overview on how you can migrate your relational databases like MySQL, PostgreSQL, Oracle, Microsoft SQL Server, and others to Amazon DocumentDB using AWS DMS.

With the launch of MongoDB 4.0 compatibility, Amazon DocumentDB (with MongoDB compatibility) now supports performing transactions across multiple documents, statements, collections, and databases. Transactions simplify application development by enabling you to perform atomic, consistent, isolated, and durable (ACID) operations across one or more documents within an Amazon DocumentDB cluster. Common use cases for transactions include financial processes, fulfilling and managing orders, and building multi-player games. In this post, I show you how to use transactions for common uses cases.

AWS Database Migration Service (DMS) announced support of Amazon Managed Streaming for Apache Kafka (Amazon MSK) and self-managed Apache Kafka clusters as target. With AWS DMS you can replicate ongoing changes from any DMS supported sources such as Amazon Aurora (MySQL and PostgreSQL-compatible), Oracle, and SQL Server to Amazon Managed Streaming for Apache Kafka (Amazon MSK) and self-managed Apache Kafka clusters.
In this post, we use an e-commerce use case and set up the entire pipeline with the order data being persisted in an Aurora MySQL database. We use AWS DMS to load and replicate this data to Amazon MSK. We then use the data to generate a live graph on our dashboard application.

In this post, I describe a simple way to transfer data from ElastiCache for Redis clusters to other AWS database services using AWS Lambda functions.

This post summarizes what’s new in Amazon DocumentDB 4.0 and show you how to get started with Amazon DocumentDB 4.0 and transactions using an AWS Cloud9 environment.