You’ve probably heard of them, but if you haven’t: Elasticsearch and Kibana are data management products created by Elastic. Elasticsearch is a search engine based on Lucene, and Kibana provides a user interface for exploring and visualizing data. Both are part of the Elastic Stack, a collection of open source tools for collecting and analyzing data that has enjoyed a rise in popularity in recent years and now boasts a number of notable users, including Facebook and Netflix.
Recently, I’ve spent some time fiddling around with open data sets in Elasticsearch and Kibana. I’ve found that they work well with Essentia, creating a seamless workflow for data organization and analysis. Continue reading to see how it all comes together.
We are pleased to announce the release of Essentia 3.1.1!
This release includes significant improvements to the functionality of the UI and CLI from the previous version, including:
- Ability to run scripts from within the UI by entering scripts directly or loading from Git repository
- Data categorization options that allow you to do preliminary preprocessing, file list caching and simple to configure regex date format feature.
- File upload, download and delete ability directly through the Explorer.
- Essentia in-memory database, UDB, now supports SQL like queries.
Visit the release notes to read more about the new features, or try the latest release today.
Although Essentia is available as a standalone software product, we also use it daily in our marketing analytics
service offered under a SaaS model.
Like a lot of other people, we like the machine learning and data mining libraries available to R
to help explore and analyze data. But like everyone else, we face the burden of having to first clean, parse, and reduce large amounts of data before being able to use those analytic tools.