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Elasticsearch analytics // native connector

Elasticsearch analytics without an ODBC driver or a warehouse copy.

Elasticsearch is a search engine. It was never meant to be the place your reporting runs from. Knowi queries your indexes in their own language, joins them with SQL, NoSQL and REST sources with no ETL in between, and puts dashboards, alerts and plain-English questions on top.

Elasticsearch JSON queries nested objects and arrays join across indexes SOC 2 Type II

Elasticsearch Analytics with Native Integration

Knowi is a unified AI data analytics platform that natively integrates with Elasticsearch, no ETL required. Perform Elasticsearch analytics at scale: query across indexes, run advanced analysis, and join Elasticsearch with NoSQL, SQL, or REST API sources using Knowi’s powerful data virtualization engine. Instantly visualize results or use Knowi's private AI engine to extract insights from your Elasticsearch data. Built for speed, flexibility, and modern data complexity.

See Knowi on your cluster

What you leave with

  • A working dashboard on one of your own indexes, built during the call.
  • A read on which nested mappings and aggregations carry over as they are.
  • A straight answer on whether Kibana already covers what you need.
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
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
Interactive filters
Drilldowns
Choose from 40+ visualization options

Secure AI-powered Data Analytics

Share and embed dashbaords
“My team tested various data visualization tools like PowerBI, Domo, Sisense and Knowi emerged as the top choice for its speed in creating useful dashboards and ease of use for non-technical users. While we selected Knowi as a viz tool, I’m also happy we did so from a data engineering perspective. Knowi’s architecture models data thoughtfully, which I’d credit for enabling a collection of elegant features.”

Phil Bryant

VP Business Intelligence, MacroFab

📆 Book your 30 minute demo

The premise

Why Elasticsearch analytics breaks inside a standard BI tool

Your indexes hold nested JSON with mappings that change. Conventional BI tools expect flat tables. Every workaround for that gap costs you something.

Workaround 01

The driver route

Tableau, Power BI and Excel reach Elasticsearch through Elastic's SQL ODBC driver. Elastic's own limitations page spells out the price: nested fields can only be referenced by sub-field, one nested field per query, no multi-nested documents. Your mapping gets flattened to fit a table.

Workaround 02

The warehouse route

A nightly job copies each index into Postgres or Snowflake, flattening on the way. Now there are two copies of the data, a pipeline someone owns, and dashboards a day behind the index.

Workaround 03

The Kibana route

Kibana reads Elasticsearch and nothing else. That is fine for one index and a technical user. The moment the orders index needs the customer table in Postgres next to it, there is no join to make.

Knowi takes none of the three routes. It speaks Elasticsearch's query language, keeps nested data nested, and joins at query time.

Driver route vs native

What changes when the BI tool speaks Elasticsearch

The left column is any SQL-first tool sitting behind the ODBC driver. The right column is Knowi's native Elasticsearch connector.

Dimension
SQL tool via ODBC driver
Knowi
Query language
SQL translated by the driver. Whatever does not map to a table is out of reach.
Elasticsearch JSON queries as written: aggregations, search templates, highlighting. Or a visual builder that generates them from the index and its field samples.
Nested JSON
Sub-field references only. One nested field per query, and no multi-nested documents.
Nested objects and arrays auto-detected. A plus sign next to the field lets you drill into them and pick what to analyze.
Across indexes
Each index is a table. Combining two is a job for the warehouse.
Join across indexes inside one dataset, no copy in between.
Beyond Elasticsearch
A separate connection with its own driver. No join between the two.
Join Elasticsearch with SQL and NoSQL databases and REST APIs at query time, across data centers or cloud providers.
Where the data sits
Flattened through the driver, or copied out to a warehouse first.
Queried where it lives. Direct execution against the cluster, or results stored on a schedule in Knowi's Elastic Store to keep load off the cluster.
Deployment
Depends on the tool.
Knowi cloud or on-premise. A Cloud9Agent option pulls from a cluster inside your network. SOC 2 Type II certified.

Nothing on the right requires you to change the cluster. The tool adapts to Elasticsearch, not the other way round.

See it on your indexes →
Reason 01

Query Elasticsearch in its own language.

Connect with the cluster URL, credentials and version. Knowi pulls the list of indexes with field samples. From there you drag fields into the visual builder, or open the query editor and write Elasticsearch JSON queries directly: bool queries, terms and date histogram aggregations, search templates with parameters, highlighting on matched fields.

No ODBC driver. No SQL plugin in the middle. No pre-defined schema.

query / orders-2026 / elasticsearchdirect
customer.address.citynestedkept
line_items[]arraykept
terms: statusaggas written
date_histogram: created_ataggas written
Direct execution or Elastic Store
real-time against the cluster, or stored on a schedule
Reason 02

Join across indexes, then across databases.

Two indexes become one dataset without a copy. Then that dataset joins the customer table in PostgreSQL, the events collection in MongoDB, or a REST API, at query time. How the cross-database joins work is documented on its own page.

This is the part the driver route and the Kibana route both give up on. It is also the part most Elasticsearch reporting questions actually need.

Your indexes and everything next to them
orders (index)
events (index)
PostgreSQL
MongoDB
Snowflake
REST APIs
Knowi
joins across indexes and sources at query time
No ODBC driver. No flattening. No warehouse copy.
Reason 03

Put dashboards, alerts and questions on top.

Interactive filters and drilldowns. Trigger alerts on a condition, delivered to email, Slack, Teams or a webhook with the results attached, so a downstream system can act. Natural-language questions across datasets, and AI agents that turn a plain-English request into a query.

Dashboards share by URL, export as PDF reports, or embed in your own application with single sign-on.

alerts / elasticsearch datasets
error_rate above 2% for 5 minSlack
orders down 30% vs 7-day averagewebhook
index refresh failedemail
"Average latency by region last week"
asked in plain English, answered from the dataset
Elasticsearch reporting

Elasticsearch reporting that leaves the cluster alone

The usual objection to reporting on a production cluster is load. Knowi gives you three ways to control it.

Real-time on large indexes

Direct passthrough queries

Dashboard filters set the metric and the dimension, and Knowi translates the selection into an Elasticsearch aggregation that runs on the cluster. Aggregate an 800 million record index in place instead of pulling it out to group later.

Scheduled

Stored datasets

Run the query once or on a schedule and keep the results in Knowi's Elastic Store. Long-running queries stop hitting the cluster on every dashboard load. Incremental pulls update the stored dataset.

Governed

Search templates

Point Knowi at an Elasticsearch search template and pass user input as parameters. The query-building logic stays in the template, out of the end user's hands.

Elasticsearch dashboards

Elasticsearch dashboards for people who do not write query DSL

The engineer connects the cluster once. Everyone else works from indexes, fields and questions.

01 Connect the cluster

Elasticsearch URL, user, password, custom headers, server version. Test the connection and save. Direct, over an SSH tunnel, or through the Cloud9Agent running inside your network. AWS-hosted Elasticsearch connects the same way.

02 Build from the index

Knowi lists your indexes with field samples. Drag fields into the visual builder, drill into nested objects with the plus sign, and preview the dataset before you save. Add filters and drilldowns on the dashboard.

03 Ask instead of build

Search-based analytics lets anyone type a question and get a chart, with auto-complete drawn from the datasets they can see. It works inside Slack and Microsoft Teams too.

Kibana

Where Kibana fits, and where it stops

Some Knowi customers keep both and pick by application. Here is the honest split.

Keep Kibana for
  • Cluster monitoring and log exploration by the team that runs Elasticsearch.
  • Single-index visualizations for technical users who write the query DSL themselves.
  • Anything that ships with the Elastic Stack and never needs another data source.
Use Knowi when
  • A report needs an index next to a SQL table, a NoSQL collection or an API.
  • Business users need to ask questions without learning Elasticsearch syntax.
  • Dashboards have to live inside your own product, with alerts that fire webhooks.

The full comparison lives on its own page: Kibana alternatives. If you are still choosing, start with how to choose an Elasticsearch analytics tool.

From a customer

“My team tested various data visualization tools like PowerBI, Domo, Sisense and Knowi emerged as the top choice for its speed in creating useful dashboards and ease of use for non-technical users. While we selected Knowi as a viz tool, I’m also happy we did so from a data engineering perspective. Knowi’s architecture models data thoughtfully, which I’d credit for enabling a collection of elegant features.”

Phil Bryant, VP Business Intelligence, MacroFab

Teams running analytics on Knowi

Verizon Telstra Lockheed Martin
Questions

What buyers ask about Elasticsearch analytics before the call

What is Elasticsearch analytics, and why is it hard in a normal BI tool?

Elasticsearch analytics means running business reporting on the documents in your Elasticsearch indexes: counts, sums, trends, and breakdowns by field. It is hard in a conventional BI tool because those tools expect flat tables and SQL, while Elasticsearch stores nested JSON and answers aggregations through its own query DSL.

Does Knowi need an ODBC driver or a SQL plugin for Elasticsearch analytics?

No. Knowi connects to the cluster with its URL, credentials and version, and sends Elasticsearch JSON queries directly. You can write the query DSL yourself, including aggregations, search templates and highlighting, or let the visual builder generate it from the index and its field samples.

How does Knowi handle nested JSON in Elasticsearch analytics?

Nested objects and arrays are auto-detected and marked with a plus sign next to the field. You drill into them and select the fields you want to analyze, without flattening the document first. Elastic's SQL layer, by contrast, reaches nested data by sub-field only, one nested field per query.

Can I join Elasticsearch indexes with SQL or NoSQL databases?

Yes, and across indexes too. Knowi joins datasets at query time across the same or different sources, so an Elasticsearch index can be joined with PostgreSQL, MySQL, MongoDB, a data warehouse or a REST API without moving any of them.

I run AWS-hosted Elasticsearch. Does Knowi still connect?

Yes. Knowi connects natively to AWS-hosted Elasticsearch as well. The connector takes a server version setting, so bring your version to the call and we will confirm the setup on your cluster.

How does Knowi compare with Kibana for Elasticsearch analytics?

Kibana visualizes what is in Elasticsearch and is a good fit for single-index, technical use. Knowi covers the same visualizations and adds joins across indexes and with other databases, natural-language questions, alerts to webhooks, and embedding in your own product. Some customers run both. The detailed comparison is on our Kibana alternatives page.

See Elasticsearch analytics running on your own indexes.

Thirty minutes on a real cluster. You leave with a working dashboard on one index, and a clear read on what joins to what.

See Knowi on your Elasticsearch data →

Bring the index, the mapping, and whatever sits next to it.

Elasticsearch Analytics Features

Search, Graph, Traffic, Conversion

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.
Server Databases Synchronize

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.

barcode search square

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.
mongodb square

Embed Elasticsearch Database 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.
content delivery

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.
Alarm, Clock, Time, Warning

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.

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?

a

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

Knowi

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  • 40+
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Kibana

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  • 17
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Confused about which ElasticSearch Analytics tool to choose for your team?

Read our detailed comparison of different tool and then make a choice.

Hundreds of companies trust Knowi to unify their analytics

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Frequently Asked Questions

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.

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

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.

Need to scale beyond Kibana for Elasticsearch Analytics?

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