Metabase vs Jaime Comparison | StackShare | StackShare

Jaime vs Metabase

Overview

Metabase

Stacks928

Followers1.2K

Votes271

GitHub Stars44.4K

Forks6.0K

Jaime

Stacks0

Followers9

Votes0

Jaime vs Metabase: What are the differences?

What is Jaime? Turn SQL into powerful dashboards. Easily and quickly create shareable reports with charts and tables by running SQL queries directly to your database.

What is Metabase? An open-source business intelligence tool. Metabase 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.

Jaime and Metabase can be primarily classified as "Business Intelligence" tools.

Metabase is an open source tool with 15.6K GitHub stars and 2.09K GitHub forks. Here's a link to Metabase's open source repository on GitHub.

Detailed Comparison

Metabase	Jaime
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.	Easily and quickly create shareable reports with charts and tables by running SQL queries directly to your database.
-	Get started fast - Just connect to your database and start querying, set up everything in 1 minute.; Protect your data - Jaime works on your computer and all conections to the database are made locally, our servers never hit your database; Share with your team - Get a shareable link for a dashboard and let your team see all the insights.
Statistics
GitHub Stars 44.4K	GitHub Stars -
GitHub Forks 6.0K	GitHub Forks -
Stacks 928	Stacks 0
Followers 1.2K	Followers 9
Votes 271	Votes 0
Pros & Cons
Pros 62 Database visualisation 45 Open Source 41 Easy setup 36 Dashboard out of the box 23 Free Cons 7 Harder to setup than similar tools	No community feedback yet
Integrations
PostgreSQL MongoDB Amazon Redshift MySQL Microsoft SQL Server	MySQL PostgreSQL SQLite Google BigQuery

What are some alternatives to Metabase, Jaime?

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.

Redash

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.

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.