Operations | Monitoring | ITSM | DevOps | Cloud

In today’s data-driven world, the ability to collect and query data from multiple sources has become a very important consideration. With the rise of IoT, cloud computing and distributed systems, organizations face the challenge of handling diverse data streams effectively. It’s common to have multiple databases/data storage options for that data. For many large companies, the days of storing everything in the singular database are in the past.

With the release of InfluxDB 3.0, one of the big questions is: how does it compare to previous versions of InfluxDB? We have begun benchmarking InfluxDB 3.0 with production workloads to start giving users more insight into the benefits of adopting InfluxDB 3.0. In this post, we look at recent benchmarks comparing InfluxDB 3.0 to InfluxDB Open Source (OSS) 1.8.

We want machines in good working order, making products of superior quality. This isn’t news. But what is newsworthy is that routine maintenance can still lead to more downtime than necessary. Not all maintenance programs are created equally. Keeping capital equipment running doesn’t exist inside a vacuum of chance. Outside the fraction of unavoidable catastrophes, there’s much power in the decision-making process.

Grouped aggregations are a core part of any analytic tool, creating understandable summaries of huge data volumes. Apache Arrow DataFusion’s parallel aggregation capability is 2-3x faster in version 28.0.0 for queries with a large number (10,000 or more) of groups.

Okay, we are back for Part 2! Last time we discussed the new community Python library for InfluxDB 3.0. Let’s talk about a bolt-on application that uses the client library as the core of its development, the InfluxDB 3.0 Python CLI.

Community Client libraries are back with InfluxDB 3.0. If you would like an overview of each client library then I highly recommend checking out Anais’s blog on their status. In this two-part blog series, we do a deep dive into the new Python Client Library and CLI. By the end, you should have a good understanding of the current features, how the internals work, and my future ideas for both projects.

DevOps and DevSecOps have gained more attention in recent years in the world of software development. While both of these methodologies emphasize the agile development process and team collaboration, there are some key differences that distinguish them. Understanding these distinctions is critical for software development teams and organizations to determine which methodology is best suited to their requirements. In this article, we’ll learn about the difference between DevOps and DevSecOps.

In software development, GitOps and DevOps are prominent techniques for enhancing collaboration and automating software delivery processes. While these two models aim to improve the efficiency of the software development life cycle, they differ in their core principles and implementation.