Elasticsearch allows you to store, search, and analyze large amounts of structured and unstructured data. This speed, scale, and flexibility makes the Elastic Stack a powerful solution for a wide variety of use cases, like system observability, security (threat hunting and prevention), enterprise search, and more. Because of this flexibility, effectively architecting your deployment’s data storage for scale is incredibly important.

Apache Arrow, a specification for an in memory columnar data format, and associated projects: Parquet for compressed on disk data, Flight for highly efficient RPC, and other projects for in-memory query processing will likely shape the future of OLAP and data warehousing systems. This will mostly be driven by the promise of interoperability between projects, paired with massive performance gains for pushing and pulling data in and out of big data systems.

In this article, we’ll learn about the Elasticsearch flattened datatype which was introduced in order to better handle documents that contain a large or unknown number of fields. The lesson examples were formed within the context of a centralized logging solution, but the same principles generally apply. By default, Elasticsearch maps fields contained in documents automatically as they’re ingested.

One of the most common dashboards for metric visualization and alerting is, of course, Grafana. In addition to logs, we use metrics to ensure the stability and operational observability of our product. This document will describe some basic Grafana operations you can perform with the Coralogix-Grafana integration. We will use a generic Coralogix Grafana dashboard that has statistics and information based on logs. It was built to be portable across accounts.

The world is full of linear relationships. When one apple costs $1 and two apples cost $2, it's easy to figure out the price of any number of apples. But what happens when you have 100s of data points? What if your data source is noisy? That's when it's helpful to use a technique called linear regression. In this article Julie Kent shows us how linear regression works, and walks through a practical example in Ruby.

I’m very pleased to announce that we’ve secured an additional $35 million in funding, bringing our total capital raised to $62.5 million. Intel Capital led our series C fund, along with support from SoftBank Ventures Asia, Samsung NEXT and La Maison. Existing investors Disruptive Technologies L.P., Aleph and Redline Capital Management also participated. Over the past year, we doubled our revenue.

There are several ways to write and query InfluxDB v2 (either open source or Cloud). You can use the HTTP API, Telegraf and any of 200+ plugins, or a client library. However, if you’re specifically looking to build an application with a fast way to fetch data concurrently with an easy binary deploy then — you guessed it — you’d probably want to use the InfluxDB Go Client.

Sitting here in my home office reflecting the potential problems the world faces both in the short term and longer term, I can’t help but think back to my career before coming to Splunk. That time was spent on the ground working ‘in the real world’, maintaining the operational and security state of systems and networks. I can empathise with the huge pressures the entire IT chain from CIOs, CISOs, IT Managers and IT admins are under right now.

We’re excited to announce the release of Elastic Cloud Enterprise (ECE) 2.5! This release improves the experience of managing your deployments with a dedicated coordinating layer, support for snapshot lifecycle management (SLM), and more.

In light of COVID-19 related office closures, one thing we’ve seen and heard repeatedly is the “abandoned NOC.” People that are responsible for finding, escalating and resolving problems in your infrastructure and applications quickly are now having to work very differently. Two-minute hallway conversations are replaced with time-consuming emails, Slack, and virtual calls.