DEVOPSdigest

Redgate is introducing two new machine learning (ML) and artificial intelligence (AI) powered capabilities in its test data management and database monitoring solutions.

With these new offerings, Redgate continues to provide ingeniously simple solutions, while maintaining data protection across the database management process.

As David Gummer, Redgate CPO, comments: “AI has the potential to bring real value to every business, but when we introduce AI and ML into database management, we must also counter any risks it introduces. At Redgate, we’ve taken an approach to introduce AI innovation in a way that delivers value without lowering standards, particularly around how data is used and shared. With the introduction of the AI capabilities in Redgate Monitor and Redgate Test Data Manager, we’re removing the bottlenecks and errors that come with manual processes, freeing up time for teams to create new value, and keeping data even more secure.”

An AI synthetic data generation capability is being added to Redgate Test Data Manager. In Redgate’s offering, the data a user inputs and the data it generates is only ever used by their local version of the capability and stays in their own data environments, addressing customer concerns about data being used to train AI/ML models, or any proprietary data leaving their environments.

Using ML algorithms to understand patterns, relationships and distribution characteristics within data, Redgate Test Data Manager will generate new data that mirrors these properties, so that users can create intricate datasets that closely mimic real-world data patterns. This provides developers and testers the accurate, representative data they need without any data being copied from or leaving production, satisfying data privacy and maintaining data integrity.

Similarly, a new ML capability will be introduced to Redgate’s long established and popular monitoring solution, Redgate Monitor, which already offers real-time performance monitoring for SQL Server and PostgreSQL.

The customizable alerts and diagnostics for databases will be enhanced further by using ML to identify which operational and performance alerts are normal background noise, and which are critical and need to be prioritized. With every organization placing different demands on its database estate, this is a standout capability that tailors Redgate Monitor to each customer’s particular requirements, reducing the time teams spend manually configuring and maintaining alerts.

With database estates becoming too large and complicated for a one-size-fits-all monitoring approach, Redgate Monitor will also use the ML capability to raise dynamic alerts based on patterns in metric data. By matching alerts to the real usage seen on monitored databases, it will improve uptime and make alerts far more relevant, avoiding alert fatigue.

The introduction of the AI capabilities follows Redgate’s own journey exploring where, when, why and how AI can be introduced into software development and other business practices safely and intelligently, with guardrails around personal and sensitive data. This echoes in many ways the experience of every organization, with an early highlight from Redgate’s latest State of the Database Landscape survey highlighting that data security and privacy is the top AI concern among 61% of respondents, a rise from 41% in last year’s survey.

The AI synthetic data generation capability in Redgate Test Data Manager is being released today as a beta program. Organizations using SQL Server, MySQL or PostgreSQL on-premises are invited to join the program to experience the feature and provide input before its launch in Q1 of 2025.

The ML capability in Redgate Monitor is now in Early Access Program (EAP) prior to its planned launch in Q1 of 2025. Organizations can access a fully-functional free trial of Redgate Monitor.