Category: Migration

The transition from SQL-based access patterns to a DynamoDB API-driven approach presents opportunities to optimize how your application interacts with its data layer. This final part of our series focuses on implementing an effective abstraction layer and handling various data access patterns in DynamoDB.

In this post, we explore strategies for designing DynamoDB data models, including entity identification, table design decisions, and relationship modeling approaches. We examine practical scenarios comparing different modeling strategies, helping you make informed decisions for your specific use case.

This is the first part of a series exploring how to effectively migrate from SQL to DynamoDB. We will examine how to analyze existing database structures and access patterns to prepare for migration, focusing on schema analysis, query patterns, and usage metrics that inform DynamoDB data model design.

In this post, we explore how a leading AWS customer in the learning services industry successfully modernized its legacy SAP ASE environment by migrating to Amazon Aurora PostgreSQL-Compatible Edition. Partnering with AWS, the customer engineered a comprehensive migration strategy to transition from a proprietary system to an open source database while providing high availability, performance optimization, and cost-efficiency.

In this post, we provide the steps to migrate a PostgreSQL database from Google Cloud SQL to RDS for PostgreSQL and Aurora PostgreSQL using the pglogical extension. We also demonstrate the necessary connection attributes required to support the database migration. The pglogical extension works for the community PostgreSQL version 9.4 and higher, and is supported on RDS for PostgreSQL and Aurora PostgreSQL as of version 12+.

In this post, we show you essential post-migration tasks to perform after migrating your SQL Server database to Amazon EC2 and how to automate this activity by using Cloud Migration Factory on AWS (CMF), such as validating database status, configuring performance settings, and running consistency checks. Additionally, we explore how the CMF solution can automate these essential tasks, providing efficiency, scalability, and heightened visibility to simplify and expedite your migration process.

In Oracle, UDTs can have member functions written in PL/SQL that are integrated directly into the UDT. In contrast, PostgreSQL currently doesn’t allow member functions within UDTs. In this post, we dive deep into these differences and provide guidance for a smooth migration, helping ensure that the integrity of your data models is maintained throughout the process. We will also walk you through the details of converting complex member type functions in the multi-nested UDT from Oracle to PostgreSQL.

In this post, we explore key strategies and AWS tools to help you successfully migrate your high-throughput relational database while minimizing business disruption.

In this post, we discuss how Heroku migrated their multi-tenant PostgreSQL database fleet from self-managed PostgreSQL on Amazon Elastic Compute Cloud (Amazon EC2) to Amazon Aurora PostgreSQL-Compatible Edition. Heroku completed this migration with no customer impact, increasing platform reliability while simultaneously reducing operational burden. We dive into Heroku and their previous self-managed architecture, the new architecture, how the migration of hundreds of thousands of databases was performed, and the enhancements to the customer experience since its completion.

This is a three-part series in which we discuss the end-to-end process of building a data lake from a legacy SQL Server database. In this post, we show you how to build data pipelines to replicate data from Microsoft SQL Server to a data lake in Amazon S3 using AWS DMS. You can extend the solution presented in this post to other database engines like PostgreSQL, MySQL, and Oracle.