December 1 saw the introduction in New Zealand of the Privacy Act 2020 which not only brings increased protection for individuals but also has some new implications for businesses, including increased fines for non-compliance and the reporting of serious privacy breaches.

Looking to level up your performance tuning game? Check out three new videos on tempdb, unstable query plans, and identifying blocking chains with SQL Monitor. I got to work with some of my favorite folks from the SQL Monitor team last week to build some new workload patterns for our online monitoring demo site. Here are three videos sharing some highlights of performance tuning features in SQL Monitor.

18 months ago, Redgate embarked on a new and ambitious journey with the acquisition of Flyway. It’s been quite a ride since then and we thought we’d end 2020 with an update on what’s been happening with the world’s most popular open source migrations framework for database deployments. We’ve learned a lot, and I wanted to share that with you in this post.

Aurora, a hosted relational database service available on the Amazon cloud, is a popular solution for teams that want to be able to work with tooling that is compatible with MySQL and PostgreSQL without running an actual MySQL or PostgreSQL database. In order to leverage Aurora’s benefits fully, it’s critical to log and analyze the various types of monitoring data that are available from an Aurora environment.

In this article, we will explain how to monitor an Oracle Database with Prometheus using an exporter to generate metrics. Also, we will review the main metrics that you should monitor on resource usage and performance, and what to alert on to detect issues and incidents in your Oracle Database.

GroundWork Monitor makes it simple to monitor the health of Oracle databases, whether the need is simple monitoring of availability or for capacity planning purposes. Oracle databases may be monitored either directly on the Oracle host or from a different host, using the GroundWork Distributed Monitoring Agent (GDMA). In both scenarios, SQL queries are used to provide the data from the database.

Optimizing SQL queries is always fun—except when it isn’t. If you’re a MySQL veteran and have read the title, you already know where this is heading 😉. In that case, allow me to regale the uninitiated reader. This is the story of an (apparently) smart optimization to a SQL query that backfired spectacularly—and how we finally fixed it. It started off with a customer noticing that a SQL query was running slowly in their environment.

Performance monitoring is great because it lets you see whether your application is fast or slow, and which parts need speeding up. For Node developers, those “parts” are most often endpoints handling incoming requests. Since the introduction of our performance monitoring offering in July 2020, Node devs have been able to use the Sentry SDK, @sentry/node, to measure the total time it takes to process each request, but we made some significant improvements since then.

Traditional database and application monitoring used to mean talking about a single server, in a single data center, in a single location. Many monitoring tools were—and still are—built around this single data center, single node idea. And while we’ve progressed to cloud-native applications, our tools haven’t quite caught on.