vmagent supports both the pushing and pulling (scrape) of metrics and here are examples of high availability setups for both cases.

Development teams are under increasing pressure to keep up with the demand for faster delivery of database changes, while ensuring code quality isn’t compromised. This pressure comes from both business requirements and industry expectations. The 2021 Accelerate State of Database DevOps report reveals that the number of teams who are elite performers is increasing year-on-year (making up 26% of those in the study) and these teams have more frequent code deployments and a lower change failure rate.

Like all Norse gods and Marvel villains, every open source project has a good origin story. The new episode of “Grafana’s Big Tent” podcast provides that behind-the-code look at building Grafana Mimir, the horizontally scalable, highly performant open source time series database that Grafana Labs debuted in March.

The desire to release features faster and deliver value to customers sooner is prompting many IT teams to adopt DevOps and Agile practices. By automating parts of the development process where possible and working in shorter cycles to release small changes more often, those teams that do well experience fewer errors and produce higher quality code. All of which is good news, until it comes to the database.

We’re excited to announce that we’ve recently contributed Redis monitoring support to the OpenTelemetry collector. You can check it out here! You can utilize this receiver in conjunction with any OTel collector: including the contrib collector, the observIQ’s distribution of the collector, as well as Google’s Ops Agent, as a few examples.

There are many types of data compatible with SQL Server, and it’s important to understand what they are to avoid issues with non-compatible data types. Understanding the compatible data types is also fundamental to understanding the data type precedence, which determines what type of data will result when working with objects of two different types.

Since we launched Grafana Mimir — the most scalable, most performant open source time series database in the world — we have answered many of your questions about our latest open source project, including how to pronounce it. (All together now: /mɪ’mir/.) We have also walked through how we scaled Grafana Mimir to 1 billion active series.

Organisations of different scales and forms want to harness the power of data to identify new business opportunities and improve current business operations. Organisations that use data effectively can hold a potential advantage – the ability to make faster and more informed business decisions. However, working with data can be a long-standing problem for businesses and functions, especially in data management and software development.

Grafana Mimir, our new open source time series database, introduces a horizontally scalable split-and-merge compactor that can easily handle a large number of series. In a previous blog post, we described how we did extensive load testing to ensure high performance at 1 billion active series. In this article, we will discuss the challenges with the existing Prometheus and Cortex compactors and the new features of Grafana Mimir’s compactor.

Since acquiring SentryOne in October 2020, the SolarWinds team has been regularly updating our Microsoft® Partners and customers about the upside these combined offerings bring to the IT community and the marketplace. Of course, SentryOne has long been recognized for its SQL Server and Microsoft Data Platform tools, with an impressive portfolio of category-leading solutions.