Have you ever had trouble working with Elasticsearch clusters? You’re not alone. In this post, I will discuss a problem I’ve encountered working with large Elasticsearch clusters and how I solved it. I will share a lot of knowhow on major technical Elasticsearch concepts, some diagrams for illustration, and of course a cool solution! In particular, I will go into Elasticsearch nodes, indices, and shards.
Let’s start off with some good news: since 7.9.0, your Elastic Workplace Search deployment has been collecting and logging product usage data for you and your team. Usage data like, what your users are searching for, what links they're actually clicking on, and which searches are falling short. And better yet, in a future release we’ll be putting a prebuilt Workplace Search analytics dashboard at your fingertips in Kibana, one of the most powerful visualization tools available.
We're excited to announce that indices created in Elasticsearch 7.10 will be smaller. Bigger isn't always better, and our internal benchmarks reported space reductions up to 10%. This may not seem like much for small use cases, but it's huge for teams handling (and paying for cloud storage of) petabytes of data.
Alicorn Global Ventures has completed an investment of $3 million in Anodot. Recently included in Forbes’ Top 20 Machine Learning Startups to Watch and a leading vendor in the fast-expanding AI analytics space, Anodot is helping companies such as Vimeo, Xandr, Atlassian and T-Mobile to leverage artificial intelligence to surface business incidents much faster and prevent loss.
Using data to predict and prevent IT outages and issues is a growing best practice—especially as advances in monitoring software have made it easier to deliver analytics in a timely manner. IT predictive analytics, once known as IT operations analytics (ITOA), is still nascent in many organizations, but it’s far more streamlined than it used to be when one needed to export data sets to specialized analytics tools such as Tableau or Microsoft PowerBI.
With the release of Kibana 7.10, dashboards have gained a powerful new feature: URL drilldowns that let you instantly click into any predefined webpage from a visual in a dashboard. Now you can build Kibana dashboards that provide data-driven insights and allow direct actionable paths to the systems you use every day. To learn more about URL drilldowns, be sure to join us for the upcoming webinar, How to build dashboards that drive insight and action in Kibana.
As you may know, InfluxDB is available in multiple forms, including InfluxDB Open Source and InfluxDB Cloud. Customers will sometimes ask us, “If I’m using InfluxDB Cloud, is there any reason I should also consider using InfluxDB open source?” It’s a fair question.
Data-driven decision-making is a method based on identifying and analyzing critical metrics and figures to gain insights about key issues and produce a workable solution. An essential aspect of this decision-making process is monitoring analytics that serves to quantify the performance of interconnected systems and resources for enhanced performance visibility and informed decision-making.
Data analysis is incredibly useful for all kinds of businesses and also has academic and hobbyist applications. Nonetheless, it’s still possible to fall into numerous traps when trying to accurately interpret your data. That’s why we’re giving you a list of the top 8 common data analysis mistakes to avoid at all costs. Our first expert Jitin Narang, CMO at TechAHead contributed the following five top data mistakes to avoid:
