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  5. Looker vs Metabase vs Superset

Looker vs Metabase vs Superset

OverviewDecisionsComparisonAlternatives

Overview

Looker
Looker
Stacks632
Followers656
Votes9
Metabase
Metabase
Stacks928
Followers1.2K
Votes271
GitHub Stars44.4K
Forks6.0K
Superset
Superset
Stacks420
Followers1.0K
Votes45

Looker vs Metabase vs Superset: What are the differences?

Introduction

In this markdown, I will provide the key differences between Looker, Metabase, and Superset. Below are the six specific differences between these three data visualization tools.

  1. Data Sources and Integration: Looker offers extensive integrations with various data sources, including databases, cloud services, and third-party tools. Metabase supports fewer data sources compared to Looker but is still capable of connecting with popular databases. Superset, on the other hand, provides a wide range of connectors but may require additional configuration for integrating with some data sources.

  2. Ease of Use: Looker focuses on providing a user-friendly interface with drag-and-drop features and a simple query language, making it easy for non-technical users to generate reports and visualizations. Metabase comes with a more intuitive interface, suitable for users with basic SQL knowledge. Superset, as an open-source tool, requires users to have a good understanding of SQL and data modeling concepts to utilize its advanced features.

  3. Advanced Analytics and Visualization: Looker offers advanced analytics capabilities, including predictive modeling and statistical analysis, allowing users to uncover insights from their data. Metabase provides basic visualization options, suitable for simple reporting and data exploration tasks. Superset offers powerful visualization features and supports a wide range of chart types, making it suitable for complex data visualization requirements.

  4. Collaborative Features: Looker provides collaboration features such as data sharing, commenting, and data permissions, allowing teams to work together on data analysis and reporting. Metabase also supports collaborative features but with limited capabilities compared to Looker. Superset offers basic collaborative capabilities but may require additional customization and configuration for advanced collaboration needs.

  5. Security and Governance: Looker offers robust security and governance features, including data encryption, single sign-on (SSO) support, and fine-grained access controls. Metabase provides basic security features but may require additional configuration for more advanced security requirements. Superset, being an open-source tool, requires users to implement security measures according to their specific needs.

  6. Cost and Licensing: Looker is a commercial tool and requires a license for usage. Metabase is an open-source tool and is free to use, making it a more cost-effective option for smaller organizations. Superset is also an open-source tool, enabling organizations to customize and use it without incurring any licensing costs.

In summary, Looker offers extensive data integration, advanced analytics, and collaboration features with a user-friendly interface, while Metabase provides a simpler, more cost-effective solution for basic reporting and data exploration needs. Superset offers powerful visualization capabilities and customization options, making it suitable for complex data visualization requirements at no licensing cost.

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Advice on Looker, Metabase, Superset

Mohan
Mohan

CEO at UPJAUNT

Nov 10, 2020

Needs adviceonFirebaseFirebaseGoogle BigQueryGoogle BigQueryData StudioData Studio

We are a consumer mobile app IOS/Android startup. The app is instrumented with branch and Firebase. We use Google BigQuery. We are looking at tools that can support engagement and cohort analysis at an early stage price which we can grow with. Data Studio is the default but it would seem Looker provides more power. We don't have much insight into Amplitude other than the fact it is a popular PM tool. Please provide some insight.

497k views497k
Comments
Vojtech
Vojtech

Head of Data at Mews

Nov 24, 2019

Decided

Power BI is really easy to start with. If you have just several Excel sheets or CSV files, or you build your first automated pipeline, it is actually quite intuitive to build your first reports.

And as we have kept growing, all the additional features and tools were just there within the Azure platform and/or Office 365.

Since we started building Mews, we have already passed several milestones in becoming start up, later also a scale up company and now getting ready to grow even further, and during all these phases Power BI was just the right tool for us.

353k views353k
Comments
Michael
Michael

CTO at Barsala

Oct 2, 2020

Needs advice

Our engineering team is deciding which data warehouse to integrate with our system, and the BI tool to interface with it.

Preliminary question - Is it best practice to try and consolidate all data to be analyzed in one location (warehouse) then have the BI tool just interface with that one source to draw insights? Know some BI tools can connect to multiple but not sure if that's a crutch until teams are able to create a single destination for all of their data

Business Requirements

  • We're looking to create dashboards for each company KPI - with the primary KPI as the highlight of the dashboard, then other downstream metrics that impact it alongside of it
  • We're looking to sync data across the platforms we work with: Stripe, Twilio, Sendgrid, Salesforce, Facebook Ads, Google Ads, Paypal, Business Amazon account (not AWS)
  • For the BI tool, we want to be able to share dashboards, connect different API's and databases, have flexible date ranges, and a nice to have is easy to interface with if team members don't know SQL

Current stack

  • Segment to route user events to Google Adwords, Facebook Ads, Mixpanel, and S3
  • Mixpanel to analyze web and mobile metrics
  • Fullstory for enhanced mobile and web visibility
  • Salesforce as a CRM - majority of our data lies within here

Current thoughts

  • AWS Redshift seems to be well adopted, integrate with most tools, and we're already building on AWS so it seems to make sense. BigQuery seemed more expensive and Snowflake didn't seem terrible but wasn't in AWS ecosystem
  • Looker has looked the most impressive on the BI tool side, but open to discussion
  • We're looking to do this alongside other projects with an in-house engineer and a contractor - we're a bit limited on the technical resources and we're looking to at least get a first pass in and eventually enhance the integration as we have bandwidth

Guidance / advice is appreciated, even if it's only for data warehousing or BI tools specifically (and not both)

6.15k views6.15k
Comments

Detailed Comparison

Looker
Looker
Metabase
Metabase
Superset
Superset

We've built a unique data modeling language, connections to today's fastest analytical databases, and a service that you can deploy on any infrastructure, and explore on any device. Plus, we'll help you every step of the way.

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'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.

Zero-lag access to data;No limits;Personalized setup and support;No uploading, warehousing, or indexing;Deploy anywhere;Works in any browser, anywhere;Personalized access points
-
A rich set of visualizations to analyze your data, as well as a flexible way to extend the capabilities;An extensible, high granularity security model allowing intricate rules on who can access which features, and integration with major authentication providers (database, OpenID, LDAP, OAuth & REMOTE_USER through Flask AppBuiler);A simple semantic layer, allowing to control how data sources are displayed in the UI, by defining which fields should show up in which dropdown and which aggregation and function (metrics) are made available to the user;Deep integration with Druid allows for Caravel to stay blazing fast while slicing and dicing large, realtime datasets;
Statistics
GitHub Stars
-
GitHub Stars
44.4K
GitHub Stars
-
GitHub Forks
-
GitHub Forks
6.0K
GitHub Forks
-
Stacks
632
Stacks
928
Stacks
420
Followers
656
Followers
1.2K
Followers
1.0K
Votes
9
Votes
271
Votes
45
Pros & Cons
Pros
  • 4
    Real time in app customer chat support
  • 4
    GitHub integration
  • 1
    Reduces the barrier of entry to utilizing data
Cons
  • 3
    Price
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
Pros
  • 13
    Awesome interactive filtering
  • 9
    Free
  • 6
    Wide SQL database support
  • 6
    Shareable & editable dashboards
  • 5
    Great for data collaborating on data exploration
Cons
  • 4
    Link diff db together "Data Modeling "
  • 3
    It is difficult to install on the server
  • 3
    Ugly GUI
Integrations
No integrations available
PostgreSQL
PostgreSQL
MongoDB
MongoDB
Amazon Redshift
Amazon Redshift
MySQL
MySQL
Microsoft SQL Server
Microsoft SQL Server
No integrations available

What are some alternatives to Looker, Metabase, Superset?

Cube

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

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

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

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

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

Shiny

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

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

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.

Periscope

Periscope

Periscope is a data analysis tool that uses pre-emptive in-memory caching and statistical sampling to run data analyses really, really fast.

Tableau

Tableau

Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.

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