Operations | Monitoring | ITSM | DevOps | Cloud

Welcome to another blog in our series on cost management and optimisation in Elasticsearch Service. In previous installments, we looked at hot-warm architecture and index lifecycle management as ways of managing the costs associated with data retention and at managing replicas as a means of optimising the structure of your Elasticsearch Service deployment. Be sure to check out the other blogs in the series for additional tips to help you as you build out your deployment.

Our Telegraf Starlark Processor Plugin is an exciting new processor in Telegraf 1.15 that gives you the flexibility of performing various operations in Telegraf using the Starlark language. What is Starlark, you ask? Starlark (formerly known as Skylark) is a language intended for use as a configuration language. Starlark is a dialect of Python. Like Python, it is a dynamically typed language with high-level data types, first-class functions with lexical scope, and garbage collection.

Last week we released Telegraf 1.15 with new plugins for network monitoring and a large number of processors to help with your data ingestion. All packages were written in Go 1.14.5. This all couldn’t have been done without the 50+ community members who contributed to writing plugins, fixing bugs, reviewing code, and everything else to help make Telegraf better! Here’s a quick look into new plugins and features we launched in Telegraf 1.15.

Hopefully you caught our Splunk Developer Spring 2020 Update in May, if you haven’t yet what are you waiting for? It introduces many updates from Splunk, including Splunk’s latest simulation tool — SimData. SimData is the best way to simulate correlated data sets for your Splunk apps. Here, we’ll cover the basics, and we’ve provided some helpful links at the bottom of this post for more details. We’ve got your back.

Simple, yet powerful metrics to determine the health and protection status of your Spectrum Protect environment in a single pane of glass.

As well as being a search engine, Elasticsearch is also a powerful analytics engine. However, in order to take full advantage of the near real-time analytics capabilities of Elasticsearch, it is often useful to add structure to your data as it is ingested into Elasticsearch. The reasons for this are explained very well in our schema on write vs. schema on read blog post, and for the remainder of this blog series, when I talk about structuring data, I am referring to schema on write.

I recently spoke with Alex Skrivseth, the Operations Manager at The Shed App, and discovered how he’s using InfluxDB to monitor the current levels of gas and diesel at various gas stations. Simply by extracting IoT sensor data, he has been able to provide valuable previously inaccessible data to fuel truck drivers.

This post is a brief summary of a presentation I gave recently where I deploy Elastic App Search, show off the ease of setup, data indexing, and relevance tuning, and take look at a few of the many refined APIs. It’s also written up in a codelab with step-by-step instructions for building a movies search engine app using Python Flask. The app will work on desktop or mobile and is a fast, simple, and reliable way to query the information.