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  5. Cube.js vs Looker vs Metabase

Cube.js vs Looker vs Metabase

OverviewDecisionsComparisonAlternatives

Overview

Looker
Looker
Stacks632
Followers656
Votes9
Metabase
Metabase
Stacks927
Followers1.2K
Votes271
GitHub Stars44.4K
Forks6.0K
Cube
Cube
Stacks96
Followers258
Votes30

Cube.js vs Looker vs Metabase: What are the differences?

Introduction: When comparing Cube.js with Looker and Metabase, it's essential to understand the key differences in functionality and features to make an informed decision on selecting the right tool for your analytics needs.

  1. Architecture: Cube.js is an open-source analytical API platform that allows developers to build interactive analytics features quickly, while Looker and Metabase are BI tools that provide a more user-friendly interface for non-technical users to create ad-hoc reports and visualizations. Cube.js focuses on customization and scalability, whereas Looker and Metabase offer more out-of-the-box solutions.

  2. Data Source Connectivity: Cube.js is more flexible in terms of data source connectivity, allowing users to connect to a wide range of databases and services such as BigQuery, Redshift, and Elasticsearch. Looker and Metabase also support multiple data sources, but they might require additional setup or configurations to connect to certain databases.

  3. Customization and Embedding: Cube.js provides extensive customization options to tailor analytics features to specific business needs, including embedding analytics directly into existing applications. Looker and Metabase offer some level of customization but may not be as flexible as Cube.js in terms of embedding analytical features.

  4. Community Support and Documentation: Cube.js has a growing community of developers contributing to its ecosystem and providing support, whereas Looker and Metabase have more established communities and comprehensive documentation for troubleshooting and learning resources.

  5. Cost Structure: Cube.js is open-source and free to use, making it a more cost-effective option for organizations looking to implement custom analytics solutions. Looker and Metabase have commercial pricing models based on users and data usage, making them potentially more expensive for larger deployments.

  6. Scalability and Performance: Cube.js is designed for scalability and performance, allowing for fast query processing and handling large volumes of data efficiently. Looker and Metabase can also handle large datasets, but Cube.js may offer better performance optimization capabilities for complex analytics use cases.

In Summary, understanding the key differences between Cube.js, Looker, and Metabase in terms of architecture, connectivity, customization, community support, cost structure, and performance scalability is crucial for selecting the right analytics tool for your organization.

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Advice on Looker, Metabase, Cube

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)

6.14k views6.14k
Comments

Detailed Comparison

Looker
Looker
Metabase
Metabase
Cube
Cube

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.

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

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
-
* Pre-aggregation; * Caching; * Data modeling; * APIs; * Works with any relational database;
Statistics
GitHub Stars
-
GitHub Stars
44.4K
GitHub Stars
-
GitHub Forks
-
GitHub Forks
6.0K
GitHub Forks
-
Stacks
632
Stacks
927
Stacks
96
Followers
656
Followers
1.2K
Followers
258
Votes
9
Votes
271
Votes
30
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
  • 8
    API
  • 6
    Visualization agnostic
  • 6
    Caching
  • 6
    Open Source
  • 4
    Rollups orchestration
Cons
  • 1
    Cannot use as a lib - only HTTP
  • 1
    Incomplete documentation
  • 1
    No ability to update "cubes" in runtime
  • 1
    Poor performance
  • 1
    Doesn't support filtering on left joins
Integrations
No integrations available
PostgreSQL
PostgreSQL
MongoDB
MongoDB
Amazon Redshift
Amazon Redshift
MySQL
MySQL
Microsoft SQL Server
Microsoft SQL Server
Amazon Redshift
Amazon Redshift
Google BigQuery
Google BigQuery
Microsoft SQL Server
Microsoft SQL Server
Snowflake
Snowflake
Presto
Presto
MySQL
MySQL
PostgreSQL
PostgreSQL
Microsoft Azure
Microsoft Azure
Oracle
Oracle
Amazon Athena
Amazon Athena

What are some alternatives to Looker, Metabase, Cube?

Superset

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.

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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