Try this live Elasticsearch analytics example

You can use this Elasticsearch example instance below to see how to query and visualize Elastic data with Knowi. Hit the Show Me button to give it a try. You can also change the settings and customize the query.

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Kibana Alternative

One of the challenges companies face when using Elasticsearch for business intelligence is that Elasticsearch manages data in JSON documents and has no support for SQL.

This means traditional BI tools like Power BI and Tableau don't work with Elasticsearch without a lot of help. That help comes from development teams and massive engineering efforts to move Elasticsearch data into a relational database.

Kibana is a good solution for more technical users where a single Elasticsearch index is the only source of data for visualizations.

But how often does that happen these days? If you're a typical company, you have a diverse data stack that includes Elasticsearch and a good number of other database technologies.

This is where Knowi comes in.

Unlike with Kibana dashboards, with Knowi you can visualize data across multiple indexes. You can dynamically blend data from other sources, like relational data stores or REST-APIs. And you can accelerate your Elasticsearch analytics projects by avoiding custom development.

Knowi natively supports SQL-style queries even when working with NoSQL datasources like Elasticsearch. So the problem of getting Elasticsearch to work with traditional BI tools is eliminated.

How does Knowi compare with Kibana?

Knowi Kibana
Native Integration to Elasticsearch
Supports AWS Elasticsearch
Number of Supported Visualizations 30+ 17
Integrated Machine Learning
Share and Embedd Dashboards
Blend Across Indexes
Blend with Other NoSQL or Relational Data
Natural Language Queries

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Check out some of our recent Elasticsearch analytics blog posts

Want to read more about integrating Elasticsearch with Knowi? Check out these blog posts where we go much more in depth.

Native Analytics On Elasticsearch With Knowi - Tutorial

Table of Contents Introduction Creating Your First Visualization Connecting to Elasticsearch Writing Your First Query Creating Your First Visualization Drilldowns Multi-Index Joins Joining Your Indexes

Read More »

Elasticsearch: What it is, How it works, and what it's used for

Updated: March 26, 2020 Introduction When people ask, "what is Elasticsearch?", some may answer that it's "an index", "a search engine", an "analytics database", "a

Read More »

Grafana vs Kibana vs Knowi: Battle Royale 2020

Intro: Grafana vs Kibana vs Knowi In today's digital world when a person uses the term "Big Data", the first thing which comes to mind

Read More »

Elasticsearch Analytics Features

01.
Native Elasticsearch analytics

You simply connect Knowi to Elasticsearch and start writing queries. Knowi is the only complete BI solution that is fully native to Elasticsearch and supports nested objects and arrays. No ODBC drivers, no SQL layer in the middle, no pre-defined schemas, no ETL. No mess. No fuss.

02.
Cross database joins

Join Elasticsearch data with NoSQL, Relational, RDBMS, and APIs on the fly across data centers or multiple cloud providers, eliminating costly ETL processes that move and SQL-ify your Elasticsearch data.

03.
Search-based analytics

Transform how your company uses its data with the use of Google-search-like capabilities on top of Elasticsearch. Knowi's search-based analytics will enable your business users to perform ad-hoc analysis in real time.

01.
Embedded Elasticsearch Analytics

Build Data-Driven Applications: With just a few clicks, you can securely embed dashboards directly into your applications your business teams are already using. Users can also share Elasticsearch dashboards or email PDF reports to extend analytics reporting to offline users company wide.

02.
Machine Learning

Combine hindsight and foresight with our machine learning workbench. Integrate machine learning directly into your Elasticsearch data analysis workflows. With Knowi ML, you can automatically trigger actions based on resulting calculations. You choose to integrate your custom algorithms or tap into our library of open source algorithms.

03.
Triggers, Alerts, and Actions

Automate actions or notifications based on the results of your Elasticsearch analytics. Easily send notifications with data attached or invoke a webhook to initiate a process in a downstream application.

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You're in good company

Frequently Asked Questions

Questions and answers about analytics and reporting on Elasticsearch and Kibana

Can Knowi be used with Kibana?

Some of our customers do deploy Knowi and Kibana together and use one or the other depending on the application. But the more common case is to use Knowi as a Kibana alternative. This is because it can duplicate the things Kibana does well, but can also do analytics with multiple databases and REST APIs.

I'm using AWS Elasticsearch, can I still use Knowi?

Yes, Knowi can natively connect to AWS versions as well.

Can I do multi-type mapping in Knowi?

Yes. Although the Type field in Elasticsearch is being depreciated. So we would recommend another approach.
Not sure how to move on from the Type field? Send us an email to support@knowi.com. We would love to help you come up with a solution.

Features of using Knowi for Elasticsearch analytics & reporting

  • Cloud or On-premise Deployments
  • Plugin Elasticsearch Queries
  • Schema Discovery & Elasticsearch Query Generation
  • Join and blend data across various NoSQL and SQL based datasources
  • Direct query execution into your database to drive visualizations, or, store and track seamlessly using our scalable, schema-less, flexible cloud warehouse
  • Share and embed dashboards to internal groups or via a simple URL
  • Works on any device
  • Choose from nearly 30 different visualization options
  • Interactive filters
  • Drilldowns
  • Natural Language Processing
  • Machine Learning
  • Prediction algorithms that auto-selects the best prediction models and forecast for any dataset
  • Plug-in architecture for custom logic & custom prediction algorithms
  • Incremental data pulls and warehouse updates
  • Push API to send real-time data
  • Data Export API
  • Single Sign-On API for embedding inside your portal

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