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Short Summary: PostgreSQL can slow down when range queries and frequent data updates rely on the same indexes. This guide shows how to spot the problem and use Devart tools to reduce B-Tree index conflicts, improve query plans, and manage bi-weekly data updates in AWS Aurora.

Summary: Today, many teams are moving from MySQL to PostgreSQL as they update their database systems and plan for future growth. However, too often, there is extra work after the migration: for example checking that tables and constraints were copied correctly, tuning performance, and confirming that replication works properly. Devart’s PostgreSQL tools help DBAs with these tasks through features like Schema Compare, Data Compare, and other tools that help review and manage PostgreSQL databases.

AWS Proton is reaching end of life. If you're reading this, you probably just found out — either from the AWS console banner, your account manager, or a panicked Slack message from someone on your platform team. Here's what you need to know: your infrastructure is safe, but the tool you use to manage it is going away. You have until October 7, 2026 to find a replacement. That sounds like plenty of time. It isn't.

Update (March 2026): AWS has announced that AWS Proton will reach end of life on October 7, 2026. After that date, the Proton console, API, and pipeline management will be permanently unavailable. If you're currently using Proton, read our migration guide.

As a team, we have spent many years troubleshooting performance problems in production systems. Applications have become so complex that you need a standard methodology to understand performance. Our approach to this problem is called the Golden Signals. By measuring these signals and paying very close attention to these four key metrics, providers can simplify even the most complex systems into an understandable corpus of services and systems.

AI cost management is the practice of tracking, allocating, and optimizing the cloud infrastructure costs tied to building, running, and scaling AI workloads. It differs from traditional cloud cost optimization because AI infrastructure behaves differently at every layer of the stack. The biggest problem isn’t overspending. It’s that most organizations can’t see where their AI spending is going.

Taking a look back over the last ten years in enterprise technology, paradigm shifts are occurring more frequently. For example, the maturity of DevOps/Platform Engineering and Cloud Native infrastructure has occurred. The new frontier depending where you are in adoption is AI. As your adoption and maturity curve progress, operationalizing these paradigms become important.