Using SQL Compare with Dynamic Data Masking

SQL Compare works with dynamic data masking, detecting the differences in masks across tables and generating the T-SQL you need to deploy your masking configuration to other databases. Read more

SQL Change Automation

Using database replication with automated deployments

For the SQL Change Automation team, it’s important that we take time out from development, occasionally, to explore some of the issues our customers face when automating database deployment. Following on from previous posts about cross-database and cross-server dependencies and production database drift, this article shares some of our thoughts about how to deal with database replication. Read more

Redgate Monitor

SQL Server monitoring from the trenches: An interview with Anthony Nocentino

I recently sat down with Enterprise Architect, Anthony Nocentino, to talk about how effective monitoring can help resolve many of the issues companies face every day. I was fascinated because my career revolves around how we can improve SQL Server monitoring. Anthony’s involves actually doing it at the coalface. He’s made a name for himself Read more

We don’t need no documentation – automating schema docs in SQL Change Automation

“Understanding the existing product consumes roughly 30 percent of the total maintenance time.” Facts and Fallacies of Software Engineering by Robert L. Glass. You should be documenting your database schema. I know it, you know it. Having current, accurate documentation available accelerates time-to-resolution for faults, aids tech-to-business conversations, and is a regulatory requirement for a great number of firms. Read more

screenshot of a drift report from sql change

SQL Change Automation

Dealing with production database drift

For the SQL Change Automation team, it’s important that we take time out from development, occasionally, to explore some of the issues our customers face when automating database deployment. Following on from previous posts about cross-database and cross-server dependencies, this article shares some of our thoughts about how to deal with production database drift. If you’re Read more

SQL Change Automation

Deploying cross-database dependencies to different environments

The SQL Change Automation team here at Redgate occasionally take time out from development to explore some of the issues our customers face when automating deployment of database changes. As part of one such exercise, we took a closer look at cross-database and cross-server dependencies – these can cause problems when deploying databases to multiple environments Read more

DLM Dashboard

Monitoring your database schemas with pipelines

DLM Dashboard tracks changes to your database schemas across different environments. In this blog post, we’ll talk about organizing your databases into pipelines, and how this can help you to better understand your workflow. How was it organized before? Grouping databases by category In the first version of DLM Dashboard, databases were grouped by category Read more

Source controlling your database permissions

How to use a combination of database roles, along with rules, filters and post deployment scripts in SQL Source Control, to add the required users to the correct role, for each database. Read more

DLM Dashboard

How to get Slack alerts when your database schemas change with Slack and DLM Dashboard

Setting the scene In my last post, I explained how it’s possible to integrate DLM Dashboard with just about every service you use at work. Now that’s all very good in principal, but how do you actually go about configuring DLM Dashboard to trigger an automated task in another app or service? In this post, Read more

SQL Change Automation

Sometimes the tool just fits – using SQL Change Automation and Octopus Deploy for Data Change Control

From a business risk perspective, data change can be just as significant as code or schema change. Sometimes even more so; an incorrect static (or reference, or master) data change can drive your software’s behaviour more dangerously askew than pretty much any bug can. Imagine treating a retail customer for an investment fund as a corporate by Read more

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