Overview

Looker

Stacks632

Followers656

Votes9

Redash

Stacks338

Followers502

Votes12

Looker vs Redash: What are the differences?

The Key differences between Looker and Redash are as follows:

Visualization Capabilities: Looker offers a more robust set of visualization options compared to Redash. It provides a wide range of interactive charts, graphs, and dashboards that can be customized to meet specific business needs. On the other hand, Redash has limited visualization options, mainly focused on basic charts and tables.
Data Exploration and Analysis: Looker excels in data exploration and analysis with its powerful data modeling and querying capabilities. It allows users to easily explore data, create complex queries, and leverage advanced analytics functions. Redash, while offering data exploration features, has more limited capabilities compared to Looker, particularly when it comes to complex analytical functions.
Ease of Use and User Interface: Looker offers an intuitive and user-friendly interface, making it easier for non-technical users to navigate and create reports and visualizations. It provides a drag-and-drop interface, centralized data modeling, and a rich set of pre-built templates. Redash, while relatively user-friendly, may require more technical expertise to utilize its full potential.
Integration and Data Source Support: Looker supports a wide range of data sources and integrations, including popular databases, cloud platforms, and third-party applications. It seamlessly connects to various data repositories, making it easier to analyze and visualize data from different sources. Redash, while supporting multiple data sources, may have limitations in terms of integration options and may require custom development for specific connectors.
Collaboration and Sharing: Looker provides robust collaboration and sharing features, allowing teams to collaborate on reports, share insights, and schedule automated report deliveries. It offers granular access controls, role-based permissions, and commenting capabilities. While Redash also supports collaboration and sharing, it may not have the same level of granularity and control as Looker.
Scalability and Performance: Looker is highly scalable, capable of handling large volumes of data and concurrent user requests. It is designed to efficiently process and analyze complex data sets, ensuring optimal performance even with heavy workloads. Redash, while scalable to a certain extent, may face limitations in terms of processing large data sets or handling high user loads.

In Summary, Looker offers more advanced visualization capabilities, superior data exploration and analysis features, an intuitive user interface, extensive integration options, robust collaboration and sharing capabilities, and high scalability and performance compared to Redash.

🔥 Trending in Business Intelligence on StackShare

AI Powered Data Analysis for Smarter Decisions Business Intelligence

AI Powered Data Analysis for Smarter Decisions

Try it View Docs Alternatives

Try

Advice on Looker, Redash

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)

6.14k views6.14k

Comments

Detailed Comparison

Looker	Redash
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.	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.
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	Query Editor;Dashboards/Visualizations;Alerts;API;Support for querying multiple databases
Statistics
Stacks 632	Stacks 338
Followers 656	Followers 502
Votes 9	Votes 12
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 9 Open Source 3 SQL Friendly Cons 1 Memory Leaks 1 All results are loaded into RAM before displaying
Integrations
No integrations available	PostgreSQL Cassandra MongoDB Amazon DynamoDB Amazon RDS Amazon Athena Jira PagerDuty Prometheus Slack

What are some alternatives to Looker, Redash?

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.