Introduction

Looker is a modern Business Intelligence (BI) and data analytics platform. It uses LookML (Looker Modeling Language) to define data relationships and business logic. In addition, it helps create reusable metrics. Therefore, organizations can build scalable data models and governed dashboards.

Moreover, it ensures consistent reporting across teams. As a result, businesses gain reliable insights. Furthermore, advanced LookML development improves performance and scalability. Consequently, teams can manage complex analytics more effectively.

Learner Prerequisites

• Basic understanding of SQL and relational databases
• Familiarity with Looker interface and Explore functionality
• Knowledge of basic LookML concepts (views, explores, dimensions, measures)
• Understanding of data modeling fundamentals (joins, keys, relationships)
• Exposure to BI reporting concepts and dashboarding

Training Table of Contents

1. Advanced LookML Fundamentals

1.1 Review of Core LookML Components (Views, Explores, Models)
1.2 Structuring Large-Scale LookML Projects
1.3 Naming Conventions and Code Organization
1.4 Reusable Code with Includes and Extends
1.5 Best Practices for Maintainable LookML

2. Advanced Data Modeling Techniques

2.1 Complex Joins and Relationship Handling
2.2 Derived Tables and Persistent Derived Tables (PDTs)
2.3 Using SQL Triggers and Datagroups
2.4 Handling Slowly Changing Dimensions (SCD)
2.5 Modeling for Multi-Fact and Aggregate Tables

3. Performance Optimization in Looker

3.1 Query Optimization Techniques in LookML
3.2 Efficient Use of PDTs and Caching Strategies
3.3 Indexing and Database-Level Optimization Considerations
3.4 Reducing Query Costs and Improving Runtime
3.5 Monitoring and Debugging Performance Issues

4. Advanced Explore Development

4.1 Designing User-Friendly Explores
4.2 Field Sets, Drill Paths, and Custom Filters
4.3 Access Filters and User Attributes
4.4 Symmetric Aggregates and Fanout Handling
4.5 Custom Measures and Advanced Calculations

5. LookML Testing and Debugging

5.1 LookML Validator and Error Handling
5.2 Writing Data Tests and Assertions
5.3 Debugging SQL Queries in Looker
5.4 Version Control Integration with Git
5.5 Deployment Workflows (Dev, QA, Prod)

6. Governance and Security Best Practices

6.1 Role-Based Access Control (RBAC)
6.2 Row-Level and Column-Level Security
6.3 Data Governance with LookML
6.4 Audit Logs and Usage Monitoring
6.5 Managing Sensitive Data and Compliance

7. Advanced LookML Features

7.1 Liquid Parameters and Dynamic SQL
7.2 Templated Filters and Conditional Logic
7.3 Localization and Multi-Language Support
7.4 Embedding Looker Content
7.5 API Integration with Looker

8. CI/CD and DevOps for LookML

8.1 Git Best Practices for LookML Projects
8.2 Continuous Integration and Automated Testing
8.3 Code Reviews and Collaboration Strategies
8.4 Deployment Pipelines and Rollbacks
8.5 Managing Multiple Environments

9. Visualization and Dashboard Optimization

9.1 Designing High-Impact Dashboards
9.2 Performance Optimization for Dashboards
9.3 Custom Visualizations and Extensions
9.4 Scheduling and Delivery Options
9.5 User Experience Best Practices

10. Real-World Use Cases and Hands-On Projects

10.1 Building Enterprise Data Models
10.2 Implementing KPI Frameworks
10.3 End-to-End LookML Project Development
10.4 Troubleshooting Real-World Scenarios
10.5 Capstone Project and Review

Conclusion

In conclusion, this training builds advanced LookML skills. Moreover, it improves scalability and performance. Therefore, learners can handle complex analytics needs. As a result, they deliver high-quality BI solutions with confidence. Furthermore, they can apply these skills in real-world projects.