Looker vs Tableau: An In-Depth, Community-Informed Comparison (2024)

Which one do you choose? Another regurgitated blog post - treading the same water, Looker this, Tableau that - or a blog post sourcing opinions from real Looker and Tableau users?

If you're here, congrats - you made the right choice. Here's everything the data communities talk about when we talk about Tableau vs Looker. The screenshots used in this article are sourced from dbt community, Locally Optimistic community, and other data communities on Slack.

If you want a governed semantic layer and self-service UI, go for Looker

The overwhelming consensus view is that Looker outshines Tableau when it comes to self-service analytics, thanks to Looker's semantic data modeling layer.

Data modeling in Looker is done via Looker’s own modeling language called LookML - a proprietary language based on the SQL programming language for relational data. LookML lets data teams define and structure how data is queried and displayed. It’s a bit like SQL’s smarter, more organized cousin. Instead of writing complex SQL queries every time you need data, data team uses LookML to create reusable, easy-to-maintain models that everyone in your organization can access.

Once your data team sets up the core data models in LookML, business users can explore and analyze data without having to write a single line of code. They can simply drag and drop metrics, build custom reports, and generate insights through Looker’s user interface. Because LookML handles the heavy lifting of defining the data structure and logic, it removes the risk of errors or inconsistencies in reporting.

👉 Screenshot: Data professionals highlighting Looker's powerful data modeling and self-service features.

Having said that, most data practitioners also pointed out that Looker's price point is too hefty in comparison to Tableau, or most other BI tools. This is also the main reason that many data teams, despite wanting Looker capabilities, have to look for alternatives.

👉 Screenshot: Looker pricing discussion from 4 years ago. Considering inflation, the price today is likely higher

If you want fancy visualization, then go for Tableau

Most people agree that Tableau comes with the best variety of data visualization types that are highly interactive and beautiful in nature.

👉 Screenshots: These users pointed out that Tableau excels in data visualization.

Looker vs Tableau: Pros and Cons

Tableau Pros & Cons: What Data Professionals Said

The best thing about Tableau is its fantastic data visualization layer. You can create excellent dashboards on Tableau.

👉 Screenshot: This user thinks Tableau excels in visualization, but when it comes to more advanced data analysis, it may fall short of expectations.

Tableau Limitations:

• Tableau can cause request queue frustration as everything has to go through your data analyst. Non-technical users are not able to create reports and get insights by themselves. (When people try to have Tableau do more than Viz, it fails)
• Tableau lacks data modeling and data dictionary capabilities for Data Analysts - which means that you’ve to separately maintain your metrics definitions elsewhere (that's too much work for data teams!)
• It also lacks code version control and collaboration when building data logic and dashboard. Without proper data governance, data consumers eventually lose trust in the dashboards you have put hours into preparing.

👉 Screenshot: This user shared frustration with Tableau’s clunky workflow, pointing out that maintaining consistency and accuracy is tough because you have to recreate the same metrics with different calculations in multiple places.

Looker Pros & Cons: Tableau Pros & Cons: What Data Professionals Said

The best thing about Looker is its code-based modeling layer with self-service data exploration. Analysts can define analytics logic centrally, meaning no metric knife fight. Using an intuitive UI, business users can perform self-service data exploration and data analysis without writing code.

Looker Limitations:

• Eye-watering price point. The entry price point is around $35000/year - not many companies can afford that.
• Limited visualization.
• Steep learning curve. LookML is not easy to learn.

👉 Screenshot: This user talked about the strength of Looker's code-based analytics workflow and praised its access control as being better to Tableau's. However, they also acknowledged that Looker's data visualization options aren't as comprehensive as Tableau's.

Looker vs Tableau: Data Visualization

Tableau is renowned for its powerful and interactive data visualization capabilities. Its visual-first, drag-and-drop interface enables users to create sophisticated visualizations and dashboards with ease. Powered by its proprietary VizQL (Visual Query Language), Tableau can quickly generate charts and graphs with, offering a wide range of customization options and chart types.

Tableau Pros:

• Industry-leading, interactive, and visually stunning dashboards.
• Wide range of chart types, with customizable options.
• A large community of data visualization designers/engineers.

Tableau Cons:

• The steeper learning curve for advanced customizations.

In contrast, Looker is more data-centric and focuses less on the aesthetics of visualization. While Looker offers customizable dashboards, it lacks the rich visualization capabilities that Tableau offers out of the box. Looker prioritizes data accuracy and effectiveness over visual storytelling, making it less ideal for organizations that heavily rely on visual exploration. However, Looker also supports custom visualization using, for example, D3.js.

Looker Pros:

• Focuses on data accuracy and effectiveness of the visualizations.
• Support for custom visualization libraries.

Looker Cons:

• Lacks the richness of Tableau’s visualization options.
• Less visually oriented, which may not appeal to users focused on graphical exploration​.

For visually stunning dashboards, Tableau is the superior choice. 

Looker vs Tableau: Data Modeling

Looker’s greatest strength lies in its robust data governance model, thanks to LookML, its proprietary data modeling language. LookML enables data teams to define business metrics and rules centrally, ensuring consistent metrics across reports and dashboards. This governed approach ensures that data is interpreted consistently across the organization, eliminating the risk of discrepancies and errors in reporting. 

Looker excels in the reusability of its data models through LookML extends, allowing metrics and data relationships to be defined once and reused across different reports and dashboards.  Looker also offers Looker Blocks, pre-built models that organizations can use to accelerate their data modeling efforts.

Looker Pros:

• LookML enforces strict data governance and ensures consistent metrics across the organization. LookML also promotes reusability by allowing data models to be used across multiple reports.
• Looker Blocks offers pre-built templates to reduce the time to create models from scratch.

Looker Cons:

• Requires a dedicated data team to manage LookML, which can add complexity.
• Limited flexibility for business users to customize data on the fly.

Tableau takes a different approach to data modeling. Users can extract snapshots from their database and perform data blending and on-the-fly transformations. However, it doesn’t enforce a centralized data model like Looker. This means organizations using Tableau must manually implement governance protocols to maintain consistency across reports.

While Tableau’s flexible data model allows users to adjust data as needed, this can easily lead to inconsistencies if not properly managed. One of Tableau's key features is the Tableau Data Extract (TDE), powered by its Hyper engine, which often outperforms some top open-source analytical solutions like DuckDB or Polar. However, it’s common in Tableau to recreate the same metrics with different calculations in various places, resulting in disparate and inconsistent data.

Tableau Pros:

• Ideal for users who need quick access to data without heavy governance.
• Users can share data sources and calculated fields to get some level of reusability.
• Fast in-memory analytical processing.

Tableau Cons:

• Lack of enforced governance can lead to inconsistent metrics.
• Users may need to implement manual processes to ensure data consistency​.

If your organization prioritizes strict data governance and consistency across reports, Looker offers a more structured solution. Tableau’s flexibility is beneficial for certain use cases, but it requires more governance oversight.

Looker vs Tableau: Self-Service Analytics

Both Looker and Tableau support self-service analytics, but they do so in different ways. Tableau empowers business users to explore data independently using its intuitive drag-and-drop interface. Its visual exploration features make it easier for non-technical users to create their dashboards and reports.

Tableau Pros: A drag-and-drop interface, making it easy for business users to explore data.

Tableau Cons:

• Flexibility can lead to inconsistencies in data interpretation without proper governance.
• May overwhelm less experienced users who are unfamiliar with building complex dashboards.

In Looker, business users explore data using pre-defined data models. This ensures that users are working with accurate and governed data but may limit their flexibility in exploring raw data.

Looker Pros:

• Enforces governed self-service by restricting exploration to defined data models.
• Reduces inconsistent metrics and errors in reports.

Looker Cons:

• Less flexibility for users to manipulate data independently.
• Users may feel limited by predefined models and metrics.

Summary: Tableau offers greater flexibility and autonomy for business users who want to explore data independently. Looker provides a more governed self-service model, which may limit flexibility but ensures data accuracy and consistency.

Looker vs Tableau: Collaboration and Sharing

Both platforms offer robust collaboration features, allowing users to share insights within and outside their organizations. Additionally, Tableau offers the ability to publish dashboards to Tableau Server or Tableau Cloud for centralized access.

Tableau Pros:

• Support web edits for published dashboards.
• Ability to publish dashboards to Tableau Server or Tableau Cloud for centralized sharing.

Tableau Cons:

• Requires a subscription to Tableau Server or Tableau Cloud for full sharing capabilities.

Looker also offers strong collaboration features, with integrations into Google Workspace and Slack for sharing insights.

Looker Pros:

• Tight integration with Google Workspace and other Google tools for easy collaboration.
• Automation capabilities, including scheduled reports and alerts, streamline collaboration across teams.

Looker Cons:

• Heavily dependent on the Google ecosystem, which may not suit all organizations.
• Collaboration beyond Google products requires additional setup and customization​

Summary: Both platforms are strong in collaboration, but Looker has an edge with its automation features and tight integration into Google tools, making it a good fit for organizations already using Google Cloud.

Looker vs Tableau: Embedded Analytics

With Tableau Embedded, data teams can turn visually compelling dashboards into customer-facing products. However, it comes with a few pros and cons.

Tableau Pros:

• Robust visualization capabilities and interactivity.
• Comprehensive APIs for deep customization and integration.
• Support row-level access control.

Tableau Cons:

• As Tableau was primarily designed for non-embedded use cases, the customization regarding white-labeling capabilities is limited compared to modern embedded analytics tools.

Looker also can be embedded into existing applications with advanced customization, flexibility, and seamless integration.

Looker Pros:

• Extensive API and SDK to enable smooth integration of analytics into applications with flexibility for developers.
• Row-level security and detailed access control.

Looker Cons:

• For smaller businesses or simpler needs, the Looker features set may be excessive, and its complexity could become a barrier to efficient usage.

Looker vs Tableau: Access Control

Tableau’s access control uses permissions and roles to manage user interactions with resources like workbooks, data sources, and projects. Permissions are set at the project, workbook, and data source levels for individual users or groups. Admins can use roles (Viewer, Explorer, Creator) with predefined capabilities or customize permissions for granular control over actions like viewing, publishing, or editing. Tableau’s permission inheritance system allows items to inherit settings from their parent projects, streamlining management. Content permissions can also be locked for consistent security.

Pros:

• Supports both project-level and content-level customization.
• The inheritance system simplifies management.

Cons:

• Permissions and roles can become confusing, especially with many resources.

On the other hand, Looker’s access control revolves around three main components:

• Content Access - Who can view or manage folders and content.
• Data Access - Which data users can access, including row-level restrictions.
• Feature Access - Actions users can take, like viewing, querying, or modifying data models.

These controls are combined into roles, pairing a Permission Set (actions) with a Model Set (data access). User Attributes allow further customization. Looker also provides monitoring dashboards for user activity, content, performance, and errors.

Pros:

• Manages access at row, model, and action levels.
• Combines permissions and model sets for tailored user experiences.

Cons:

• Multi-layered system can be overwhelming for new admins or small teams.
• Requires careful planning and familiarity with LookML for effective control.

Looker vs Tableau: Advanced Analytics

When it comes to advanced analytics, Tableau supports integration with R and Python through its TabPy/R platform, making it ideal for organizations that need to run custom statistical models or machine learning algorithms within their dashboards.

Tableau Pros:

• Supports advanced analytics with R and Python integrations for data preparation and predictive modeling.
• Ideal for organizations needing custom statistical models in their dashboards.

Tableau Cons:

• Can be complex and overwhelming for non-technical users when integrating R or Python.
• Performance may decline with large, complex datasets.

Looker:
Looker integrates with cloud-based machine learning platforms, particularly within the Google Cloud ecosystem (ML Accelerator, BQML). It doesn't directly support R or Python but offers native integration with Google’s AI/ML tools, making it suitable for cloud-native analytics. Looker data can be exported to data science environments via its SDK.

Looker Pros:

• Seamless integration with Google Cloud AI/ML tools for cloud-native machine learning workflows.
• Efficiently handles large-scale data analytics in the cloud.

Looker Cons:

• Requires reliance on Google Cloud’s ecosystem for advanced analytics.

Summary: For advanced analytics with R or Python, Tableau might be the better choice. For those leveraging Google Cloud’s AI/ML capabilities, Looker might be more suitable.

Looker vs Tableau: Different Classes of BI Tools

In our Modern Analytics Setup Guidebook, we also classified Tableau and Looker into different classes of BI tools.

Tableau is a non-modeling BI tool. Looker is a modeling BI tool.

With non-modeling BI (Tableau, Mode, Redash), you model the data using a separate data modeling tool, or you have to hardcode the business logic directly into the report itself. If it’s the latter, you stand the risk of getting into business logic discrepancy, because now there are multiple places in your BI system that contain duplicates of the same logic.

With modeling BI (Looker, Holistics), you benefit from a semantic modeling layer alongside BI functionality. Because of that, your entire logic is centralized in the data modeling layer, thus greatly increasing metric consistency and logic reusability across the organization. An additional benefit of having a modeling layer baked in the same BI tool is maintaining context: you can trace the full lineage of report data back to its original form because the tool plays a part in every transformation along the way.

Tableau is an in-memory BI tool. Looker is an in-database BI tool.

• Tools like Looker run SQL queries on top of a powerful database. The heavy lifting is done by the database itself; the connected BI tool merely grabs the results of generated queries and displays it to the user.
• In contrast, Tableau (and PowerBI) assumes the analyst will take data out of the central data warehouse and run analysis on their own machines. In terms of workflow, this is similar to taking data out of a central system and then dumping it into Excel. The performance of the analytical tool is thus limited by the power of the tool itself, along with the computational resources of the analyst’s machine.

When you are evaluating BI tools, it helps to understand which process the tool assumes you would use. Does it leverage the power of the data warehouse? Or does it assume you’re going to be pulling data out and running it on an individual analyst’s machine?

Looker vs Tableau: Why Not Both?

While Looker is famous for its governed self-service layer, it comes with limited visualizations.

On the other hand, Tableau, while famous for its flexible visualizations, lacks a central place to govern metrics and maintain analytics logic.

We don’t think you have to settle with either vanilla charts or scattering metrics.

We built Holistics to make it easier to go from governed metrics to beautiful visualizations, as if you can use both Tableau and Looker at the same time.

Holistics offers the best of Looker and Tableau:

• Canvas-based dashboards that allow you to build Tableau-like visualizations. You can have full control over every dashboard component, flexibly design your dashboards in a free-form canvas, and manage, extend, and reuse them with code.
• You can design and curate a governed self-service experience for non-technical users with a variety of interactivity controls, just like in Looker.
• Common analytical functions (period over period, Percent of Total, etc) with more coming are 1-click operations without writing custom formulas and logic as in Looker/Tableau.
• You can leverage analytics engineering best practices - version control, CI/CD, refactoring, etc., to set up a self-service BI platform that is reliable and easy to maintain.

Like always, the choice is yours.

Disclaimer: All of the screenshots used in blog posts are sourced from expert's discussions dbt slack and Locally Optimistic Slack - we encourage you to join these communities to see full conversations.