It’s been an exciting year here at Coralogix. We welcomed our 2,000th customer (more than doubling our customer base) and almost tripled our revenue. We also announced our Series B Funding and started to scale our R&D teams and go-to-market strategy. Most exciting, though, was last September when we launched Streamaⓒ – our stateful streaming analytics pipeline. And the excitement continues!

There’s an insidious disease increasingly afflicting DevOps teams. It begins innocuously. A team member suggests adding a new logging tool. The senior dev decides to upgrade the tooling. Then it bites. You’re spending more time navigating between windows than writing code. You’re scared to make an upgrade because it might break the toolchain. The disease is tool sprawl.

AIOps is a DevOps strategy that brings the power of machine learning to bear on observability and system management. It’s not surprising that an increasing number of companies are now adopting this approach. AIOps first came onto the scene in 2015 (coincidentally the same year as Coralogix) and has been gaining momentum for the past half-decade. In this post, we’ll talk about what AIOps is, and why a business might want to use it for their log analytics.

Elasticsearch is a distributed search and analytics engine used for real-time data processing of several different data types. Elasticsearch has built-in processing for numerical, geospatial, and structured text values. Unstructured text values have some built-in analytics capabilities, but custom text fields generally require custom analysis. Built-in text analysis uses analyzers provided by Elasticsearch, but customization is also possible.