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.

Redis is an in-memory key-value data store that offers fast performance, flexible data structures, and multi-model databases, allowing it to handle a variety of use cases. Redis Enterprise enhances open source Redis with features designed to run distributed applications at scale, such as multi-tenancy, tiered data storage, active-active cluster replication, and support for up to five 9s of availability.

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 walked through how we scaled Grafana Mimir to 1 billion active series. And we will be hosting webinars to showcase cutting-edge features like query sharding and the two-stage compactor.

Enterprises evolve and transform into data-driven businesses, which take valuable insights from the data collected to grow and develop their business. This means that massive chunks of data are collected every second and companies search for ways to process it faster, secure and more accurately. The more data is processed, the smarter is the organisation and the greater potential for data-driven decisions is available.

A NoSQL database provides a mechanism for data storage and retrieval, without using the tabular relations associated with relational databases. Originally referred to as "non-SQL" or "non-relational" databases, NoSQL databases are increasingly used in big data and real-time web application environments. NoSQL systems are also sometimes called “Not only SQL” to emphasize that they may support SQL-like query languages or sit alongside SQL databases in polyglot-persistent architectures.

A NoSQL database provides a mechanism for data storage and retrieval, without using the tabular relations associated with relational databases. Originally referred to as "non-SQL" or "non-relational" databases, NoSQL databases are increasingly used in big data and real-time web application environments. NoSQL systems are also sometimes called “Not only SQL” to emphasize that they may support SQL-like query languages or sit alongside SQL databases in polyglot-persistent architectures.

Data masking is a technique of hiding sensitive data from certain users who should not be able to view or access the actual values. This post will look at the reasons why you might choose to implement data masking and discuss both dynamic and static data masking techniques, and when it is appropriate to use each of these.

In the first part of my DevOps 101 series, we tackled the fundamentals of What, who why and how. In this follow-up, I’m going to focus on how to gain buy-in across your organization. That said, who do you need buy-in from? DevOps is the union of people, processes and products to enable continuous delivery of value to our end users, according to Microsoft’s Donovan Brown. That’s a good explanation, because the focus is not on tooling, or processes, or even just people.

Since the beginning of the year, our team has been busy working with the open source community of VictoriaMetrics users and our customers as we continuously enhance and improve Vicky! Thanks to everyone who has contributed with their feedback, questions, feature requests, bug reports, etc.