In this blog, we will provide a brief introduction to data governance and show how to implement it on AWS using DataZone. We will walk through a practical example involving a multi-account setup to manage and share data stored in S3 and Redshift, highlighting key steps and best practices along the way.

Moving Amazon Redshift to a new account and region might seem difficult, but it doesn’t have to be. You might need to follow regulations or reorganize your teams. In this guide, we will show you step by step how to move your Redshift data to a different account and region. After reading this guide, you will know how to do Redshift migrations easily, with minimal downtime and secure data. Let’s start and make your Redshift move simple!

AWS Redshift Query Editor v2 is a web-based tool that allows analysts to securely explore, share, and collaborate on data using SQL within a common notebook interface. It simplifies querying data with SQL and visualizing results with charts and graphs in just a few clicks.

In my previous blog, we explored enabling cross-account data sharing using AWS Lake Formation with Terraform. In this post, we’ll dive deeper into enhancing that setup with data filters. Lake Formation data filtering allows for column-level, row-level, and cell-level security. This blog will focus specifically on implementing cell-level security to fine-tune data access controls.

In this blog post, we’ll delve into the process of writing data into InfluxDB v3 using Go. Our focus will be on harnessing the capabilities of the influxdb3-go client library, particularly its support for annotated structs. Through a practical example, we’ll demonstrate how to convert a slice into a structured format and efficiently batch write it into InfluxDB.