Looker Blocks: building blocks for scalable and governed BI

As your Looker development environment expands with new projects, Explores, and dashboards, maintaining consistency, reusability, and efficient collaboration becomes increasingly challenging. Looker Blocks offer a compelling solution, enabling you to package and share LookML code in a Git repository. This article explores the power of Looker Blocks in building a scalable and governed BI platform.

To ensure your BI platform is governed and scalable, check out our article on Looker BI & Data Governance.

Unlocking reusability and efficiency

Looker Blocks are reusable pieces of LookML code that encapsulate specific data models, business logic, or visualization components. They function as building blocks for your Looker projects, promoting code reusability and accelerating development. The benefits are significant:

• Consistency: ensure standardized definitions and calculations across your Looker environment, eliminating discrepancies and enhancing data integrity.
• Efficiency: reduce development time by reusing pre-built components instead of starting from scratch, enabling teams to focus on more strategic initiatives.
• Collaboration: foster seamless collaboration among developers, promoting knowledge sharing and best practices within the organization.

• Governance: centralize control of data models and calculations, ensuring data quality and security across your BI platform.

• AI-ready integrations: connect Blocks to BigQuery and Vertex AI to generate predictive metrics and insights directly from reusable components

Harnessing the power of Git

Looker Blocks are hosted in Git repositories, bringing the benefits of version control, branching, and code review to your Looker development process. This enables:

• Traceability: track changes and modifications made to LookML code, enhancing visibility and accountability.
• Experimentation: safely experiment with new ideas and features without impacting production environments.
• Collaboration: enable multiple developers to work on the same project simultaneously, improving productivity and reducing conflicts.

Use cases and applications

Looker Blocks cater to diverse use cases, including:

• Shared data models: define and share consistent data models across different Looker projects, ensuring data integrity and facilitating cross-functional analysis.
• Custom visualizations: create reusable visualization components, such as charts or maps, for a consistent and polished look and feel across your dashboards.
• Business logic: package complex calculations or business rules into reusable components, promoting efficiency and minimizing errors.

Conclusion

Looker Blocks empower you to build a scalable and governed BI platform by promoting code reusability, streamlining collaboration, and ensuring data consistency principles that Datwave actively applies when designing enterprise-grade Looker semantic layers. By integrating Git version control, Looker Blocks introduce best practices from software development into your Looker environment, an approach Datwave leverages to align BI development with modern data engineering workflows. As you scale up your BI efforts, Looker Blocks provide a framework for maintaining control, enhancing efficiency, and fostering a culture of data-driven insights, as we have seen proven across Datwave led Looker implementations in complex organizations. Embrace the power of Looker Blocks and unlock the full potential of your Looker investment, supported by Datwave’s hands-on experience in building scalable, governed, and reusable Looker architectures.

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Author

Ludovica Pierdominici | BI Specialist @Datwave

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