Looker Vs Tableau

Looker Vs Tableau – A Detailed Comparison

Looker Vs Tableau

Data is the driving force of the modern world. But data can only be best utilized if it can effectively showcase the detailed picture at the right time, with the right information. This is where the role of business intelligence tools come into action. And two big names in the world of BI are Looker and Tableau, as they both empower businesses to garner insightful information from data.

Often compared, Looker Vs Tableau is an interesting evaluation since both have a lot in common too yet have their share of differences. It is tough to finalize which tool is best for your organization.

This article throws light on the details of both the tools in terms of their pros and cons, organizations using them and their similarities and differences. Prior to that, let us have a quick overview of both.

What is Looker?

Powered by Google, Looker is a cloud-based BI tool that is meant to offer intuitive information and detailed analytics through its data modelling layer. It offers precise results by data consolidation from disparate sources, through a unified dashboard.

Data modelling by Looker helps users in creating intuitive information via reusable models. It helps in exploring data, through its dashboards and reports that create a variety of graphs, charts etc.

Looker leverages its own language – Looker Modelling Language (LookML) created around SQL for semantic data models. It is easy to grasp for novices who know basic SQL and for experts to create data models that act like readily usable applications.

What is Tableau?

Tableau is a leading data visualization and analytics tool for interpretation and understanding of data through its elaborate interface. Through its dashboards, reports, charts etc., it facilitates integration of data from multiple sources.

It can manage complicated data sets and hence is recommended for organizations that have multi-faceted data. Tableau Desktop, Tableau Server and Tableau Cloud are the three main components that lead the show.

Due to its drag and drop feature, Tableau can showcase visualization in a layered and detailed manner, making it easy for users to interpret information. Data can be segregated into different measurements and dimensions, making it easy for users to perceive.

Good Read: Top 10 Popular BI Tools for Insightful Data Analytics and Visualization

Looker Vs Tableau – Pros and Cons

Pros of Looker

  • Seamless integration into pre-defined BI solutions
  • Creation of custom applications based on user requirements
  • Embedded analytics and reports with real-time data
  • Creation of pre-built pieces of code call blocks
  • Effective version control system
  • Real-time data processing with auto refresh
  • Automated testing with CI/CD

Good Read: Best Practices to Enhance Dashboard Design and Reporting in Looker Studio

Cons of Looker

  • Higher learning curve
  • Limited data security
  • Issue managing larger datasets

Pros of Tableau

  • Can operate on-premises, in the cloud and integration with other systems
  • Create AI-driven statistical modelling with NLP
  • Easy to share insights through shared server or cloud
  • Facility to set custom notifications based on certain data conditions
  • Support for custom SQL queries
  • User friendly interface
  • Capability to blend data from disparate sources

Cons of Tableau

  • Complex management of real-time data
  • Difficult management of licenses
  • Slow performance with large datasets

Looker Vs Tableau – Organizations Using Them

  • Companies Using Looker

Gympass, The North Face, CircleCI, Square, eBay Inc, Typeform, Trendyol Group, BlaBlaCar, Deliveroo, Panasonic Corp, DigitalOcean and many more…

  • Companies Using Tableau

Red Hat, Nissan, Henkel, Verizon, Lufthansa, Honeywell, Lenovo, Hello Fresh, Chipotle, Pemco, Providence, Schwab, Deloitte and many more…

Key Comparison Between Looker Vs Tableau

The Similarities

  • Flexible, comprehensive, and scalable
  • Emphasis user collaboration and accessibility
  • Wide range of BI tools supporting advanced ML concepts
  • User friendly
  • Extensive integration with data sources
  • Facilitates decision-making by transforming huge datasets into perceptions

The Differences

Quick Comparison: Looker Vs Tableau

OverviewA browser-based intelligence tool that excels in data exploration, modelling, governance and offers an in-database architecture for real-time analytics.A BI tool that specializes in creation of visualization dashboards with pre-built dashboards, drag and drop interface, for enhanced user experience.
ScalabilityBecause it has in-database architecture, database can be scaled as data grows to manage speed.Because it has a distributed server architecture, more server nodes can be added for larger data sets.
Native LanguageLookMLVizQL
Version Control

Git-versioned semantic layer for version control

Version control with audit trails

Data Visualization

Dynamic dashboard filter feature that filters for data visualization types and specified users

Pre-made templates and infographics with a wizard feature that assists non-technical users

Data Modelling

Pre-built modelling called Looker Blocks utilized to create sophisticated query analytics

Makes use of Snowflakes and dimensional data models to enhance query performance
SecurityAdmin panel for changing security settings and two factor authenticationBasic level of security mechanism to protect reports and dashboards
Use Cases
  • Transforming sales into series of graphs, charts etc.
  • Highlighting different region of sales
  • Creating a centralized data model for data governance
  • Self-service analytics for data analysis
  • Data dictionary for documentation and semantic understanding
  • Creating graphs of monthly site visitors for traffic growth
  • Interactive dashboards and reports for data exploration and discovery
  • Ad hoc analysis and quick data visualization
  • Interactive data visualization for storytelling and communication reasons

When to Use What?

Use Looker when

  • Organization size is large with a skilled data team that can design and manage dashboards with LookML
  • There are resources who can develop dashboards for non-technical users.
  • Businesses need detailed data exploration capabilities which can be fulfilled through SQL foundation and customized queries.

Use Tableau when

  • Organization size is small with smaller data teams and a wider end user base
  • There are a greater number of non-technical users to use the system
  • There is a need for user friendliness and there are teams with varying technical expertise

On a Concluding Note

Consider any – Tableau or Looker, it is like choosing the better of the best.  They both belong to a family of popular BI tools that have both carved a niche for themselves. What must be considered is organizational factors like budget, existing infrastructure, storage preferences, use cases, size of existing team, analytical data requirements, skilled expertise, project timelines, organizational objectives etc.


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