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Looker vs dbt: What are the differences?

Introduction: Looker and dbt are both popular tools used in the realm of data analytics and business intelligence. While they both serve similar purposes, there are key differences between the two that make them unique in their own right.

1. Data Transformation Methodology: Looker primarily focuses on visualization and exploration of data through its user-friendly interface, while dbt (data build tool) is designed specifically for data transformation and modeling tasks. Dbt allows for the creation of complex data pipelines and automated workflows, making it ideal for data engineers and analysts.

2. Deployment and Infrastructure: Looker is typically cloud-based and its processing occurs in the cloud, utilizing the scalability and resources of cloud computing. Dbt, on the other hand, can be run both in the cloud and on-premises, offering more flexibility in deployment options based on organizational preferences and data security requirements.

3. Workflow Orchestration: Dbt provides robust workflow management and orchestration capabilities, allowing users to schedule and run data transformations in a systematic manner. Looker, on the other hand, lacks such extensive workflow orchestration features, focusing more on real-time interactive querying and visualization.

4. Governance and Version Control: Dbt excels in version control and data governance aspects, enabling users to track changes in data models, collaborate seamlessly, and ensure data integrity across the organization. Looker, while offering some governance features, may not provide the same level of granularity and control over the data transformation processes.

5. Learning Curve and User Skillset: Looker is known for its intuitive and user-friendly interface, making it easier for business users and non-technical stakeholders to generate insights and reports. Dbt, with its focus on data modeling and transformation, requires a certain level of SQL proficiency and technical knowledge, catering more towards data professionals and engineers.

6. Customization and Extensibility: In terms of customization and extensibility, Looker offers a range of customization options through its LookML modeling language, allowing users to tailor their analytics platform to specific business needs. Dbt, while flexible, may not offer the same level of deep customization as Looker in terms of building bespoke analytical solutions.

In Summary, Looker and dbt differ in their primary focus on data visualization vs. data transformation, deployment options, workflow orchestration capabilities, governance and version control features, user skillset requirements, and customization and extensibility offerings.

Decisions about dbt and Looker

Very easy-to-use UI. Good way to make data available inside the company for analysis.

Has some built-in visualizations and can be easily integrated with other JS visualization libraries such as D3.

Can be embedded into product to provide reporting functions.

Support team are helpful.

The only complain I have is lack of API support. Hard to track changes as codes and automate report deployment.

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Vojtech Kopal
Head of Data at Mews Systems | 3 upvotes 路 307.5K views

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.

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Pros of dbt
Pros of Looker
  • 5
    Easy for SQL programmers to learn
  • 2
  • 2
    Schedule Jobs
  • 2
    Reusable Macro
  • 2
    Faster Integrated Testing
  • 2
    Modularity, portability, CI/CD, and documentation
  • 4
    Real time in app customer chat support
  • 4
    GitHub integration
  • 1
    Reduces the barrier of entry to utilizing data

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Cons of dbt
Cons of Looker
  • 1
    Only limited to SQL
  • 1
    Cant do complex iterations , list comprehensions etc .
  • 1
    People will have have only sql skill set at the end
  • 1
    Very bad for people from learning perspective
  • 3

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What is dbt?

dbt is a transformation workflow that lets teams deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.

What is Looker?

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.

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What companies use dbt?
What companies use Looker?
See which teams inside your own company are using dbt or Looker.
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What tools integrate with dbt?
What tools integrate with Looker?

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