Looker vs Superset

Overview

Looker

Stacks632

Followers656

Votes9

Superset

Stacks420

Followers1.0K

Votes45

Looker vs Superset: What are the differences?

Introduction

This markdown code provides a comparison between Looker and Superset. The key differences between these two tools are outlined below.

Data sources and integrations: Looker offers a wide range of pre-built integrations for popular data sources such as Google Analytics, Salesforce, and Amazon Redshift. On the other hand, Superset has a more limited set of supported data sources and integrations.
Ease of use and user interface: Looker has a more intuitive and user-friendly interface, with drag-and-drop features and interactive visualizations that make it easy for non-technical users to explore and analyze data. Superset, although powerful, has a steeper learning curve and may require more technical knowledge to fully utilize its capabilities.
Collaboration and sharing: Looker provides robust collaboration features, allowing users to easily share reports, dashboards, and insights with teammates and stakeholders. It also offers various permission levels to control access to data and restrict certain actions. Superset, while it has basic collaboration features, lacks some of the advanced sharing and permission settings offered by Looker.
Customization and extensibility: Looker offers extensive customization options, allowing users to create custom visualizations, define complex calculations, and build tailored dashboards. It also supports the creation of custom applications using Looker's extension framework. Superset, although customizable to some extent, may not provide the same level of flexibility and extensibility as Looker.
Security and data governance: Looker has advanced security features, including user authentication, data encryption, and access controls, ensuring the protection of sensitive data. It also provides detailed auditing and logging capabilities for compliance purposes. Superset, while it has some security features, may not have the same level of enterprise-grade security and data governance as Looker.
Support and community: Looker has a dedicated support team and a large user community, offering resources, documentation, and forums for troubleshooting and learning. Superset, being open-source, relies more on its community for support, which may not be as comprehensive or immediate as the support provided by Looker's team.

In Summary, Looker offers a wider range of data sources and integrations, has a more intuitive user interface, provides advanced collaboration features, offers extensive customization options, has stronger security and data governance features, and provides dedicated support. Superset, being open-source and more focused on technical users, may have limitations in these areas.

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Manual

Advice on Looker, Superset

Mohan

CEO at UPJAUNT

Nov 10, 2020

Needs adviceon

Firebase

Google BigQuery

Data 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

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

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	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.	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
Stacks 632	Stacks 420
Followers 656	Followers 1.0K
Votes 9	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 13 Awesome interactive filtering 9 Free 6 Shareable & editable dashboards 6 Wide SQL database support 5 Great for data collaborating on data exploration Cons 4 Link diff db together "Data Modeling " 3 Ugly GUI 3 It is difficult to install on the server

What are some alternatives to Looker, Superset?

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.

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.

Periscope

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

Related Comparisons

Looker vs Superset: What are the differences?

Introduction

This markdown code provides a comparison between Looker and Superset. The key differences between these two tools are outlined below.

Data sources and integrations: Looker offers a wide range of pre-built integrations for popular data sources such as Google Analytics, Salesforce, and Amazon Redshift. On the other hand, Superset has a more limited set of supported data sources and integrations.
Ease of use and user interface: Looker has a more intuitive and user-friendly interface, with drag-and-drop features and interactive visualizations that make it easy for non-technical users to explore and analyze data. Superset, although powerful, has a steeper learning curve and may require more technical knowledge to fully utilize its capabilities.
Collaboration and sharing: Looker provides robust collaboration features, allowing users to easily share reports, dashboards, and insights with teammates and stakeholders. It also offers various permission levels to control access to data and restrict certain actions. Superset, while it has basic collaboration features, lacks some of the advanced sharing and permission settings offered by Looker.
Customization and extensibility: Looker offers extensive customization options, allowing users to create custom visualizations, define complex calculations, and build tailored dashboards. It also supports the creation of custom applications using Looker's extension framework. Superset, although customizable to some extent, may not provide the same level of flexibility and extensibility as Looker.
Security and data governance: Looker has advanced security features, including user authentication, data encryption, and access controls, ensuring the protection of sensitive data. It also provides detailed auditing and logging capabilities for compliance purposes. Superset, while it has some security features, may not have the same level of enterprise-grade security and data governance as Looker.
Support and community: Looker has a dedicated support team and a large user community, offering resources, documentation, and forums for troubleshooting and learning. Superset, being open-source, relies more on its community for support, which may not be as comprehensive or immediate as the support provided by Looker's team.

Looker vs Superset

Overview

Looker vs Superset: What are the differences?