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.