Redash vs Knowi | What are the differences? | StackShare

Knowi vs Redash

Overview

Redash

Stacks338

Followers502

Votes12

Knowi

Stacks3

Followers14

Votes0

Redash vs Knowi: What are the differences?

What is Redash? Easily query an existing database, share the dataset and visualize it in different ways. Redash helps you make sense of your data. Connect and query your data sources, build dashboards to visualize data and share them with your company.

What is Knowi? AI Driven Analytics on Modern Data. It combines Business Intelligence with integrated machine learning for advanced analytics, reporting and visualizations on structured and unstructured data.

Redash and Knowi belong to "Business Intelligence" category of the tech stack.

Some of the features offered by Redash are:

Query Editor
Dashboards/Visualizations
Alerts

On the other hand, Knowi provides the following key features:

Native NoSQL, SQL, REST API
Cloud or On-premise Deployment
Instant Visual Insights

Redash is an open source tool with 14.3K GitHub stars and 2.46K GitHub forks. Here's a link to Redash's open source repository on GitHub.

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Detailed Comparison

Redash	Knowi
Redash helps you make sense of your data. Connect and query your data sources, build dashboards to visualize data and share them with your company.	It combines Business Intelligence with integrated machine learning for advanced analytics, reporting and visualizations on structured and unstructured data.
Query Editor;Dashboards/Visualizations;Alerts;API;Support for querying multiple databases	Native NoSQL, SQL, REST API; Cloud or On-premise Deployment; Instant Visual Insights; NLP, Machine Learning, Predictions and Triggers
Statistics
Stacks 338	Stacks 3
Followers 502	Followers 14
Votes 12	Votes 0
Pros & Cons
Pros 9 Open Source 3 SQL Friendly Cons 1 All results are loaded into RAM before displaying 1 Memory Leaks	No community feedback yet
Integrations
PostgreSQL Cassandra MongoDB Amazon DynamoDB Amazon RDS Amazon Athena Jira PagerDuty Prometheus Slack	Google Analytics MongoDB Oracle InfluxDB MySQL Elasticsearch MarkLogic HBase

What are some alternatives to Redash, Knowi?

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

Power BI

It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

Mode

Created by analysts, for analysts, Mode is a SQL-based analytics tool that connects directly to your database. Mode is designed to alleviate the bottlenecks in today's analytical workflow and drive collaboration around data projects.

Google Datastudio

It lets you create reports and data visualizations. Data Sources are reusable components that connect a report to your data, such as Google Analytics, Google Sheets, Google AdWords and so forth. You can unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions.

AskNed

AskNed is an analytics platform where enterprise users can get answers from their data by simply typing questions in plain English.

Shiny

It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.