Category: Advanced (300)

Amazon DynamoDB zero-ETL integration with Amazon SageMaker Lakehouse allows you to run analytics workloads on your DynamoDB data without having to set up and manage extract, transform, and load (ETL) pipelines. In this two-part series, we first walk through the prerequisites and initial setup for the zero-ETL integration. In Part 2, we cover setting up Amazon SageMaker Unified Studio, followed by running data analysis to showcase its capabilities. We illustrate our solution walkthrough with an example of a credit card company that wants to analyze its customer behavior and spending trends.

In this post, we introduce the AWS Database Acceleration Toolkit (DAT), an open source database accelerator. DAT is an infrastructure as code solution using Terraform to simplify and automate initial setup, provisioning, and on-going maintenance activities for Amazon Aurora.

Data validation is an important process during data migrations, helping to verify that the migrated data matches the source data. In this post, we present alternatives you can use for data validation when dealing with tables that lack primary keys. We discuss alternative approaches, best practices, and potential solutions to make sure that your data migration process remains thorough and reliable, even in the absence of traditional primary key-based validation methods. Specifically, we demonstrate how to perform data validation after a full load migration from SQL Server to Amazon Aurora PostgreSQL-Compatible Edition using the PostgreSQL tds_fdw extension.

Etleap is an AWS Advanced Technology Partner with the AWS Data & Analytics Competency and Amazon Redshift Service Ready designation. In this post, we show how Etleap helps you build scalable, near real-time pipelines that stream data from operational SQL databases into Iceberg tables using AWS DMS. You can use AWS DMS as a robust and configurable solution for change data capture (CDC) from all major databases into AWS.

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+.

Organizations are increasingly seeking to modernize their database infrastructure by migrating from legacy database engines such as Microsoft SQL Server and Oracle to more cost-effective and scalable open source alternatives such as PostgreSQL. This transition not only reduces licensing costs but also unlocks the flexibility and innovation offered by PostgreSQL’s rich feature set. In this post, we demonstrate how to convert and test database code from Microsoft SQL Server and Oracle to PostgreSQL using the generative AI capabilities of Amazon Bedrock.

As companies expand globally, they must be able to architect highly available and fault-tolerant systems across multiple AWS Regions. With such scale, a company can find itself in this position when designing a caching solution across its multi-Region infrastructure. In this post, we dive deep into how to use Amazon ElastiCache for Valkey, a fully managed in-memory data store with Redis OSS and Valkey compatibility, and the Amazon ElastiCache for Valkey Global Datastore feature set.

In this post, we guide you through setting up automation for pre-upgrade checks and upgrading a fleet of Amazon RDS for PostgreSQL instances. In this solution, we use AWS Systems Manager to automate the Amazon RDS upgrade job.

In this post, we explore how pgvector 0.8.0 on Aurora PostgreSQL-Compatible delivers up to 9x faster query processing and 100x more relevant search results, addressing key scaling challenges that enterprise AI applications face when implementing vector search at scale.

In this post, we describe some of the openCypher features that have been released as part of the 1.4.2.0 engine update to Amazon Neptune. Neptune provides developers with the choice of building their graph applications using three open graph query languages: openCypher, Apache TinkerPop Gremlin, and the World Wide Web Consortium’s (W3C) SPARQL 1.1. You can use the guide at the end of this post to try out the new features that are described.