Course Introduction
The Looker and LookML Analytics Development Training Course provides comprehensive, hands-on training for professionals who need to build governed, reusable, and scalable analytics solutions with Looker. The course focuses on LookML development, semantic modeling, Explores, dimensions, measures, relationships, calculations, dashboards, testing, and performance optimization. LookML is used to describe semantic data models and business logic so Looker can generate SQL against underlying databases while allowing business users to explore governed data without managing the underlying query structure themselves.
Participants begin by learning the architecture of Looker projects and the role of the Looker IDE in analytics development. The training explains how model files, view files, Explores, dimensions, measures, and relationships work together to create a reusable analytical layer. Learners develop the ability to translate database structures and business requirements into clear LookML definitions, while understanding how modeling decisions influence the fields and analytical experiences presented to users in Looker.
A central component of the course is semantic modeling with LookML. Participants learn how to define dimensions, measures, dimension groups, filters, parameters, derived values, and business metrics in a structured and reusable way. The course emphasizes consistent metric definitions and maintainable modeling patterns so analysts and business users can explore data using governed definitions rather than repeatedly implementing business logic in individual queries. Looker documentation identifies dimensions as groupable fields and measures as fields based on aggregate functions such as COUNT, SUM, AVG, MIN, and MAX.
The program also develops practical expertise in building Explores and relationships between views. Participants learn how to select appropriate base views, define joins, manage relationship types, control field exposure, and validate query behavior. Explores serve as starting points for analytical queries, with users selecting dimensions and measures from modeled views to construct their analyses. This makes Explore design an important development activity for balancing flexibility, usability, data correctness, and performance.
Advanced LookML development is covered through reusable modeling patterns, derived tables, refinements, project organization, parameters, and dashboard development. Participants learn how to extend existing models without unnecessarily duplicating code, customize inherited modeling structures, and create maintainable analytics components. LookML refinements, for example, can adapt existing views or Explores without modifying the original LookML definition, supporting reusable projects and customized modeling layers.
The final part of the training focuses on production-oriented analytics development, including LookML validation, testing, debugging, Git-based development practices, performance considerations, dashboard design, and an end-to-end capstone project. Participants learn how to move from raw database structures to governed semantic models, user-friendly Explores, and business-facing dashboards. By the end of the course, learners will have practical experience designing a complete Looker analytics solution using maintainable LookML development practices aligned with modern Looker workflows.
Duration
5 days
Who Should Attend
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Looker developers responsible for creating and maintaining LookML models, Explores, dashboards, and governed analytics solutions.
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Data analysts who want to understand semantic modeling and develop reusable analytical definitions in Looker.
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Analytics engineers building centralized business logic and metrics on top of cloud data warehouses.
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Business intelligence developers transitioning from SQL-based reporting into Looker and LookML development.
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Data engineers who need to expose warehouse structures through governed Looker semantic models.
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BI architects designing scalable analytics platforms, semantic layers, and reusable reporting environments.
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Database developers who want to translate relational data structures into business-friendly Looker models.
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Reporting professionals developing interactive dashboards and self-service analytical experiences with Looker.
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Data professionals responsible for defining enterprise metrics, dimensions, measures, and analytical relationships.
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Technical analysts supporting governed self-service analytics and standardized business reporting.
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Professionals managing LookML projects through development, testing, version control, and deployment workflows.
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Teams seeking practical, hands-on experience building production-oriented Looker analytics applications.
Course Objectives
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Develop practical proficiency in Looker and LookML for designing reusable semantic models, governed analytics structures, Explores, dashboards, and business-facing analytical experiences.
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Understand Looker project architecture and confidently work with model files, view files, Explores, dimensions, measures, relationships, and other core LookML components.
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Build semantic models that translate database structures into consistent business definitions while reducing duplicated analytical logic across reports and user queries.
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Design dimensions, measures, dimension groups, filters, parameters, and calculated fields that accurately represent business requirements and analytical definitions.
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Create and configure Explores with appropriate joins and relationships while controlling query behavior, field exposure, usability, and analytical correctness.
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Apply advanced LookML techniques including derived tables, refinements, reusable structures, conditional logic, and parameter-driven modeling patterns.
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Develop interactive dashboards and analytical experiences that use modeled data effectively while maintaining clear navigation, meaningful metrics, and consistent business definitions.
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Apply LookML validation, testing, debugging, and development practices to identify modeling errors, incorrect relationships, unexpected query results, and maintainability issues.
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Use performance-oriented modeling techniques to reduce unnecessary query complexity, improve analytical responsiveness, and create scalable Looker implementations.
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Complete an end-to-end Looker development project that transforms a warehouse schema into a governed LookML model, usable Explores, dashboards, and production-ready analytics workflows.
Comprehensive Course Outline
Module 1: Looker Architecture and Analytics Development Fundamentals
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Understanding Looker's role as a business intelligence and governed analytics platform, including the relationship between databases, semantic models, Explores, queries, visualizations, and dashboards.
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Exploring the Looker development environment, navigation structure, permissions concepts, Development Mode, Production Mode, and the role of the Looker IDE in LookML development.
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Understanding how LookML describes dimensions, aggregates, calculations, relationships, and business logic while allowing Looker to generate SQL against supported database platforms.
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Examining the complete lifecycle from database structures and LookML modeling through Explore development, user analysis, dashboard creation, testing, version control, and deployment.
Module 2: LookML Projects, Models, and Views
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Creating and organizing LookML projects and understanding how model files and view files work together to define the analytical structure presented to Looker users.
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Building view definitions that connect analytical fields to underlying database tables or derived tables while maintaining clear and reusable modeling structures.
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Creating model definitions that identify connections, include required project files, expose Explores, and establish the foundations for governed user exploration.
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Organizing LookML files using maintainable naming, folder, inclusion, documentation, and reusable-code conventions suitable for collaborative analytics development.
Module 3: Dimensions, Measures, and Business Metrics
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Creating dimensions that represent attributes, facts, identifiers, dates, categories, and derived row-level values that users can select and filter within Explores.
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Developing measures using appropriate aggregation types such as counts, sums, averages, minimums, maximums, and other business-specific analytical calculations.
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Building dimension groups for time-based analysis and creating consistent date, time, week, month, quarter, and year analytical fields from source timestamps.
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Designing reusable metric definitions that centralize business logic and help users obtain consistent analytical results across queries, reports, and dashboards.
Module 4: Explores, Joins, and Data Relationships
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Creating Explores that provide intuitive starting points for business analysis while exposing the appropriate dimensions, measures, filters, and related views.
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Defining joins between views and selecting appropriate relationship behavior to preserve analytical accuracy when users combine fields from multiple datasets.
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Diagnosing fanouts, duplicated measures, unmatched records, unexpected row counts, and other problems caused by incorrect join logic or inappropriate relationship design.
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Applying Explore-level configuration, field organization, labels, descriptions, and usability practices that make complex analytical models easier for business users to navigate.
Module 5: Advanced LookML Calculations and Modeling
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Creating SQL-based dimensions and measures that transform warehouse data into meaningful analytical attributes and reusable business metrics.
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Working with conditional logic, filters, parameters, date logic, type conversions, and other modeling techniques for sophisticated analytical requirements.
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Developing derived tables and advanced modeling structures when analytical requirements cannot be addressed effectively through straightforward views and measures.
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Applying reusable LookML patterns that reduce duplicated logic, improve maintainability, and separate business definitions from individual user queries and dashboard implementations.
Module 6: Advanced LookML Reuse, Refinements, and Project Design
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Understanding LookML refinements and using them to customize existing views or Explores without unnecessarily changing their original definitions or duplicating entire modeling structures.
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Designing reusable modeling layers that support shared LookML components, customized implementations, and multiple analytical models across organizational teams.
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Applying constants, includes, reusable definitions, naming standards, and project organization techniques to make large LookML codebases easier to maintain.
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Evaluating model architecture decisions that affect extensibility, collaboration, governance, and the ability to adapt analytical definitions as business requirements evolve.
Module 7: Looker Dashboards and Analytics Experiences
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Creating analytical dashboards that combine modeled queries, visualizations, filters, and business metrics into structured experiences for decision support.
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Understanding how LookML-defined models and Explores provide the underlying fields and query structures used by dashboard components.
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Working with LookML dashboard definitions and configuring dashboard elements around models, Explores, dimensions, measures, filters, and visualization requirements.
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Designing dashboards around clear business questions, consistent metric definitions, intuitive filtering, appropriate visualizations, and maintainable analytical content.
Module 8: LookML Validation, Testing, Debugging, and Performance
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Using Looker development and validation capabilities to identify syntax problems, invalid references, modeling inconsistencies, relationship issues, and other LookML development errors.
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Debugging unexpected query results by tracing field definitions, joins, filters, calculations, generated SQL, and underlying database behavior.
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Evaluating query and model performance by reducing unnecessary complexity, controlling data volume, optimizing derived structures, and designing efficient analytical relationships.
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Establishing development standards for validating model changes before deployment and maintaining confidence in business-critical metrics and analytical content.
Module 9: Git, Development Workflow, Governance, and Deployment
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Understanding Development Mode and Production Mode and applying controlled development practices when creating, reviewing, testing, and deploying LookML changes.
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Using Git-based source control practices to manage LookML projects, track changes, collaborate with other developers, review modifications, and maintain development history.
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Establishing code-review standards, naming conventions, documentation practices, validation checkpoints, and deployment procedures for collaborative Looker development.
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Managing changes to semantic models carefully so new dimensions, measures, joins, and business rules can be introduced without unnecessarily disrupting existing analytics.
Module 10: End-to-End Looker and LookML Analytics Development Project
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Designing a complete Looker project beginning with a warehouse schema and translating business requirements into reusable views, dimensions, measures, and semantic definitions.
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Building a production-oriented Explore with appropriate joins, relationships, field organization, descriptions, filters, and analytical safeguards for business users.
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Developing interactive dashboards from the completed semantic model and validating that visualizations, metrics, filters, and user workflows produce the intended analytical outcomes.
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Presenting the completed analytics solution through a capstone review covering LookML architecture, modeling decisions, validation, performance considerations, governance, deployment, and recommended maintenance practices.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
