Continuous Database Monitoring is a very important aspect of enterprise applications monitoring. Database is the foundation of any application. If the performance of the database is not good then every user request can be impacted. Continuous database monitoring does provide very quick ROI. Tweaking the time consuming SQLs and any other database bottlenecks have impact on performance, scalability and availability of the entire application.

SQL performance tuning can be a tricky thing. We spend so much time focusing on our business logic, it’s easy to leave the database behind in the dust. But it’s important to keep the DB in mind to maintain quality performance. We need tools in our toolbox to tune our SQL performance. In this post, we map out six tips to help you tune SQL Server. Learn more and try Raygun free for 14 days.

IBM DB2 is a database management system that runs on a wide range of technologies, including Linux, UNIX, Windows, mainframes, and IBM Power Systems. You can use DB2 as a managed service in the cloud or deploy it in a cluster to provide high availability, making it suitable for a wide range of enterprise and customer-facing applications.

Unlike traditional SQL databases, NoSQL databases, or “non-SQL” databases, do not store their data in tabular relations. Originally designed for modern web-scale databases, they have found widespread use in present-day big data and real-time web applications. Some of the most commonly used data structures include key-value, wide column, graph, and document stores.

Database security refers to the various measures organizations take to ensure their databases are protected from internal and external threats. Database security includes protecting the database itself, the data it contains, its database management system, and the various applications that access it. Organizations must secure databases from deliberate attacks such as cyber security threats, as well as the misuse of data and databases from those who can access them.

Last week I rolled out a simple patch that decimated the response time of a Postgres query crucial to Checkly. It quite literally went from an average of ~100ms with peaks to 1 second to a steady 1ms to 10ms. However, that patch was just the last step of a longer journey. This post details those steps and all the stuff I learned along the way. We'll look at how I analyzed performance issues, tested fixes and how simple Postgres optimizations can have spectacular results.

Let’s not bury the lede here: we use Observability-Driven Development at Honeycomb to identify and prevent DB load issues. Like every online service, we experience this familiar cycle. This is not a bad thing! It’s a normal thing. Databases are easy to start with and do an excellent job of holding important data.