Testing is one of those activities that if not exhaustive will not have its complete impact on your software development process. Oftentimes developers are only concerned about testing the application layer of the system (a.k.a the codebase) and ignore testing the data layer (the database) which is also as important as testing the code itself.

Storage is one of the most critical components for any relational database management system, and getting the right storage configuration affects reliability, availability, and performance. When it comes to SQL Server storage best practices, choosing between storage hardware options has changed significantly over the last decade, but that doesn’t necessarily make choosing the correct storage options for SQL Server any easier.

Every year, the Accelerate State of DevOps Report examines the capabilities and practices that drive software delivery, operational, and organizational performance. The 2021 report from the DevOps Research and Assessment (DORA) team at Google Cloud has now been published and provides highlights from seven years of research and data from more than 32,000 professionals worldwide. So what are those highlights? Where should IT teams be focusing their efforts on their journey to DevOps?

Redis is a simple – but very well optimized – key-value open source database that is widely used in cloud-native applications. In this article, you will learn how to monitor Redis with Prometheus, and the most important metrics you should be looking at. Despite its simplicity, Redis has become a key component of many Kubernetes and cloud applications. As a result, performance issues or problems with its resources can cause other components of the application to fail.

I am currently trialing pgmetrics and pgDash for monitoring PostgreSQL databases. Here are my notes on it. pgmetrics is a command-line tool you point at a PostgreSQL cluster and it spits out statistics and diagnostics in a text or JSON format. It is a standalone binary written in Go, and it is open source. Here is a sample pgmetrics report. Rapidloop, the company that develops pgmetrics, also runs pgDash – a web service that collects reports generated by pgmetrics and displays them in a web UI.

Introducing HighPerf, our highly scalable raw storage database that facilitates smarter analytics, faster troubleshooting, and better bandwidth management.

Welcome to our latest benchmark figures for Aiven for PostgreSQL on AWS, GCP and MS Azure.

Far too often, organizations look at monitoring solutions as "nice to have", or better put, as luxury items that make the lives of IT administrators easier, but come at a cost. This series will look at ways that organizations have saved money off of their IT budgets, by bringing Foglight into their environment. The first post in this multi-part series will focus on consolidating an organizations cross platform database monitoring under a single solution: Foglight for Databases.

To make life easier for database admins and simplify DevOps, Aiven now offers a Kubernetes Operator for Aiven for PostgreSQL and Aiven for Apache Kafka services. Find out how you can use it to manage your whole service stack.

Application developers have a choice between two main categories of database: SQL (Structured Query Language) and NoSQL (Not Only SQL). SQL databases, also known as relational databases, have been in use for over 40 years. Despite their age, SQL databases remain extremely popular with developers. Of the top 10 results on DB-Engines’ list of most popular database management systems in September 2021, six were relational, or SQL-based.

SQL Server monitoring is continuous collection and analysis of usage, performance, and event metrics for Microsoft SQL Server. It’s the first step in optimizing performance for applications that depend on your data platform. Highly effective monitoring gives a bird’s-eye view of your entire data estate. It also provides the deep analytics necessary to perform root cause analysis on the most challenging performance problems.

You may have read or heard about IDC’s recent Global DataSphere Forecast, 2021-2025, which predicts that global data creation and replication will experience a compound annual growth rate (CAGR) of 23% over the forecast period, leaping to 181 zettabytes in 2025. That’s up from 64.2 zettabytes of data in 2020 which, in turn, is a tenfold increase from the 6.5 zettabytes in 2012.

Managing any database ecosystem is difficult enough: taking backups, maintaining statistics, and doing performance tuning all tax the time of the DBA or database developer. The job is complex even without considering the work you do to manage the various schema and data drifts that can occur. Unless you operate in a vacuum or within a single person organization (and even then, schema drift can occur), drift is going to manifest naturally and as the size of the environment expands.

When drift happens within a database, it can occur at a couple of different levels. Drift refers to entities—tables, views, or even data—out of synchronization with each other. This could be a difference in schema structure, data, or even operational metadata like permissions. Often, drifts happen between two different environments like development and staging databases.

Investigating your family tree can be an interesting experience. For example, what if you discovered you were related to a famous person who won a Nobel Prize or performed a heroic act? Conversely, what if you realized you had an ancestor who was an infamous criminal? Much like examining your genealogy can be an exciting adventure, looking at the family tree of your database can prove to be just as rewarding. Databases occasionally undergo a phenomenon known as drift.