Author: Harish Bannai

In this post, we show you how to deploy a serverless pipeline that creates an irreversibly redacted, queryable archive of Amazon RDS for PostgreSQL audit logs. The pipeline permanently removes Social Security numbers (SSNs), credit cards, email, names, and more than 30 types of personally identifiable information (PII) before storing clean logs in Amazon S3 for query through Amazon Athena.

In this post, we demonstrate how you can implement a comprehensive monitoring strategy for Amazon RDS for SQL Server by combining AWS native tools with SQL Server diagnostic utilities. We explore AWS services including AWS Trusted Advisor, Amazon CloudWatch Database Insights, Enhanced Monitoring, and Amazon RDS events, alongside native SQL Server tools such as Query Store, Dynamic Management Views (DMVs), and Extended Events. By implementing these monitoring capabilities, you can identify potential bottlenecks before they impact your applications, optimize resource utilization, and maintain consistent database performance as your business scales.

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.

Today AWS DMS announces support for migrating data to Amazon S3 from any AWS-supported source in Apache Parquet data format. This is one of the many new features in DMS 3.1.3. Many of you use the “S3 as a target” support in DMS to build data lakes. Then, you use this data with other AWS […]

AWS now supports new data transformation capabilities in the latest AWS Database Migration Service (AWS DMS) version 3.1.3. You can change schema, table, and column names; specify individual tablespace names for Oracle targets; and update a table’s primary and unique key on any target. DMS version 3.1.3 supports the following new data transformation capabilities: Explicit table […]