Redgate 'smartens up' database DevOps portfolio
Database DevOps company Redgate has put new machine learning (ML) and artificial intelligence (AI) capabilities in its test data management and database monitoring technologies.
With an emphasis on streamlining workflows for data science teams, Redgate says it is maintaining data protection across the database management process.
With AI adoption increasing in some areas, the company points out concerns that gravitate around inaccuracies, intellectual property and cybersecurity.
When wanting to use test data, many development teams have less than ideal access to production data due to the risk of exposing sensitive customer information and compliance concerns.
Often, it seems, teams need to generate data which is as similar to production data as possible, whether recreations of existing projects, tricky corner cases or even greenfield projects where data doesn’t yet exist in any form.
To address this, an AI synthetic data generation capability is being added to Redgate Test Data Manager.
Local data, local environments
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.
“Today we know that 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,” said David Gummer, Redgate CPO. “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.”
Using ML algorithms to understand patterns, relationships and distribution characteristics within data, Redgate Test Data Manger 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.
Real-time monitoring
A new ML capability will be introduced to Redgate’s monitoring solution, Redgate Monitor, which offers real-time performance monitoring for SQL Server and PostgreSQL.
The customisable alerts and diagnostics for databases will be enhanced by using ML to identify which operational and performance alerts are normal background noise and which are critical and need to be prioritised.
With every organisation 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.
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, with guardrails around personal and sensitive data.