Redis, as an in-memory data store, excels at providing high-speed data access and manipulation. However, without effective monitoring, the potential advantages of Redis can be compromised due to performance bottlenecks, scalability issues, and resource constraints. By closely scrutinizing key metrics, Redis monitoring allows you to proactively detect and address potential problems, ensuring the stability, reliability, and high-performance operation of your Redis environment.

We’re happy to announce a landmark 320% growth in 2023! VictoriaMetrics, our open source time series database and monitoring solution, already hit 268 million downloads this year (still counting), and received close to 13,000 stars on GitHub.

Grafana Tempo 2.3 has been unleashed upon the world, bringing with it the latest iteration of the vParquet backend! Tempo 2.3 has a little bit of everything, but the headline item here is vParquet3 and new features that improve search speeds. Watch the video above for all the details, or continue reading to get a quick overview of the latest updates in Tempo. If you’re looking for something more in-depth, don’t hesitate to jump into the changelog or our Grafana Tempo 2.3 release notes.

Containers are a lightweight, portable, and consistent way to package applications and their dependencies. Containers provide an isolated environment, ensuring an application runs reliably across different environments. Enterprises and tech-savvy individuals are using container technologies because of their benefits. However, container orchestration tools have become necessary to manage clusters with the rise in container usage.

In May 2023, Thomas Kronawitter, Head of Data-Driven Applications & Services at Grenke AG, joined Redgate CPO David Gummer at the Gartner Data & Analytics Summit to provide insights and advice based on his own digital transformation journey in financial services. This post highlights the key take-aways from David’s conversation with Thomas, including the strategies, challenges, and successes that shaped Grenke’s experience.

Welcome to the second chapter of the handbook on Anomaly Detection for Time Series Data! This series of blog posts aims to provide an in-depth look into the fundamentals of anomaly detection and root cause analysis. It will also address the challenges posed by the time-series characteristics of the data and demystify technical jargon by breaking it down into easily understandable language. This blog post (Chapter 2) is focused on different types of anomalies.

MySQL is the trusted open-source database management system for many desktop, mobile, web, and cloud applications. Monitoring the performance of MySQL is critical but as the applications expand over multi-cloud, cloud-native, and hybrid cloud, monitoring also grows in complexity. Continuous monitoring and scaling help applications take advantage of MySQL’s capabilities such as reliability, security, flexibility, availability, and performance scalability.

Microsoft Azure offers a choice of relational and non-relational database services to support a wide range of application needs and demands. Built-in intelligence helps automate management tasks like high availability, scaling, and query performance tuning to provide users with services that ensure applications are always available and performant. Many services offer essentially limitless database scale and SLAs (Service Level Agreements) usually range between 99.9-99.999% availability.

OpenTelemetry libraries can be used to monitor MongoDB interactions. In this tutorial, we will learn how we can monitor MongoDB with OpenTelemetry libraries to analyze query execution and identify performance bottlenecks. Most modern applications have distributed architecture thanks to cloud and containerization. In cloud-native applications, it is necessary to track user requests across services and components like databases.

Welcome to the handbook on Anomaly Detection for Time Series Data! This series of blog posts aims to provide an in-depth look into the fundamentals of anomaly detection and root cause analysis. It will also address the challenges posed by the time-series characteristics of the data and demystify technical jargon by breaking it down into easily understandable language. This blog post (Chapter 1) is focused on.