Learn LookML, Google Looker’s modeling language, to get complete control of the visualization process | LookML mastery
β±οΈ Length: 3.9 total hours
β 4.43/5 rating
π₯ 24,847 students
π October 2025 update
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- Course Overview
- This comprehensive ‘A-Z’ course serves as a practical blueprint for developers aiming to master Google Looker’s declarative data modeling language, LookML. It provides a foundational yet in-depth understanding of how to transform raw database schemas into intuitive, performant, and reliable data models that empower business users.
- Go beyond basic dashboard creation by learning to engineer the underlying data definitions that power Looker. This course positions you to architect robust data ecosystems, ensuring consistency, reusability, and scalability across all your analytical endeavors.
- Understand the critical role LookML plays in bridging the gap between intricate database structures and easily consumable business intelligence. You’ll gain the power to precisely define data transformations, aggregations, and relationships, thus taking unequivocal control over how data is exposed and perceived by end-users.
- Discover how to build a unified semantic layer that serves as a single source of truth for your organization’s metrics and attributes. This hands-on journey will equip you with the skills to craft sophisticated data models that drive insightful analytics and foster confident, data-driven decision-making.
- The curriculum is designed for practical application, ensuring that you not only grasp the syntax but also the architectural principles behind effective LookML development, preparing you to tackle real-world data modeling challenges.
- Elevate your data development capabilities by learning to construct dynamic and adaptable data models, enabling unparalleled flexibility and control over your visualization and reporting processes within the Google Looker platform.
- Requirements / Prerequisites
- A solid conceptual understanding of relational databases and data warehousing principles.
- Proficiency in SQL (Structured Query Language) for querying and manipulating data.
- Familiarity with programming or scripting logic, as LookML operates on a declarative code basis.
- An analytical mindset with a keen interest in data definition, transformation, and optimization.
- Access to a Google Looker instance or a sandbox environment for practical exercises (though the course itself will guide you).
- While not strictly mandatory, prior exposure to a cloud data platform (e.g., Google BigQuery) can be beneficial.
- Skills Covered / Tools Used
- Skills Covered:
- Designing resilient and modular data models following Looker’s best practices for maintainability and scalability.
- Crafting advanced data transformations, including derived tables (both persistent and transient) to optimize query performance and encapsulate complex business logic.
- Implementing sophisticated caching strategies to improve report loading times and reduce database load.
- Establishing robust data governance by defining consistent metrics, attributes, and data relationships across your organization.
- Building dynamic data views and ‘Explores’ that provide intuitive self-service analytics capabilities for business users.
- Optimizing LookML code for performance, readability, and efficient resource utilization within the Looker environment.
- Creating custom fields, parameters, and filters to enable highly interactive and personalized data exploration.
- Developing and managing complex data definitions to expose granular and aggregated data views to various user groups.
- Structuring multi-layered data models that support diverse analytical requirements, from raw data exploration to high-level executive summaries.
- Implementing collaborative development workflows and change management strategies within the Looker IDE, leveraging Git integration for seamless team projects and deployments.
- Troubleshooting, debugging, and resolving common LookML development challenges and performance bottlenecks.
- Applying advanced LookML patterns to handle intricate data scenarios and create powerful, reusable components.
- Tools Used:
- Google Looker Integrated Development Environment (IDE) for all LookML coding and project management.
- Git (integrated within Looker) for version control and collaborative development.
- SQL (as the underlying language for data interaction and transformation logic).
- The Google Looker platform itself for deploying, testing, and interacting with your developed data models.
- A relational database or data warehouse (e.g., BigQuery, Snowflake, Redshift – implicitly connected via Looker) as the data source.
- Skills Covered:
- Benefits / Outcomes
- Gain the expertise to build sophisticated and maintainable data models that serve as a reliable foundation for all your business intelligence initiatives.
- Become proficient in translating complex business requirements into elegant and efficient LookML code, directly impacting decision-making processes.
- Empower your organization with true self-service analytics by designing user-friendly and highly adaptable data exploration experiences.
- Enhance your career prospects as a specialized Data Engineer, BI Developer, or Data Analyst with a valuable and in-demand skill set in the Google ecosystem.
- Contribute significantly to improved data quality, consistency, and accessibility across various departments within your company.
- Streamline the entire data-to-insight pipeline by mastering the critical data modeling layer, reducing reliance on manual data preparation.
- Achieve full mastery over Looker’s backend development, enabling you to optimize performance, customize data experiences, and ensure data integrity.
- Foster seamless collaboration among data teams, analysts, and business stakeholders by establishing a common language and structure for data.
- PROS
- Highly practical and immediately applicable skills for modern data professionals working with Google Looker.
- Concise duration allows for rapid upskilling and integration of new knowledge into current projects.
- Exceptional student ratings and a large enrollment indicate proven quality and widespread utility of the course content.
- CONS
- Assumes a foundational understanding of data concepts and SQL, potentially requiring supplementary learning for absolute beginners.
Learning Tracks: English,Development,Data Science
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