Introduction

Looker is a modern Business Intelligence (BI) platform. It helps users explore and analyze data in real time. It uses LookML to define business logic. In addition, it ensures consistent metrics across teams.

Moreover, Looker supports fast data exploration. As a result, users can perform ad-hoc analysis with ease. Furthermore, it enables quick insights from large datasets. Therefore, organizations can make better decisions.

Learner Prerequisites

• Basic understanding of SQL and relational databases
• Familiarity with Looker interface and navigation
• Knowledge of dimensions, measures, and filters
• Basic understanding of data analysis concepts
• Exposure to BI tools is helpful

Table of Contents

1. Introduction to Data Exploration in Looker

1.1 Overview of Looker Explore Interface
1.2 Understanding Data Models and Explores
1.3 Dimensions, Measures, and Filters
1.4 Query Structure in Looker
1.5 Use Cases for Data Exploration

2. Working with Explores

2.1 Navigating Explores Efficiently
2.2 Selecting Fields and Building Queries
2.3 Applying and Managing Filters
2.4 Sorting and Limiting Data
2.5 Saving and Reusing Queries

3. Performing Ad-hoc Analysis

3.1 Creating On-the-Fly Reports
3.2 Using Quick Calculations
3.3 Pivoting Data for Insights
3.4 Comparing Metrics Across Dimensions
3.5 Identifying Trends and Patterns

4. Advanced Query Techniques

4.1 Using Custom Fields and Table Calculations
4.2 Creating Derived Metrics
4.3 Using Parameters for Dynamic Analysis
4.4 Handling Complex Data Scenarios
4.5 Optimizing Queries for Accuracy

5. Data Filtering and Drilldowns

5.1 Advanced Filtering Techniques
5.2 Using Filter Expressions
5.3 Drilldowns for Detailed Analysis
5.4 Linking Data for Deeper Insights
5.5 Exploring Related Data Sets

6. Visualization for Exploration

6.1 Choosing the Right Visualization
6.2 Switching Between Table and Chart Views
6.3 Using Visual Cues for Insights
6.4 Quick Dashboard Creation from Explores
6.5 Enhancing Data Interpretation

7. Saving, Sharing, and Collaboration

7.1 Saving Looks and Queries
7.2 Sharing Insights with Teams
7.3 Scheduling Reports
7.4 Exporting Data
7.5 Collaboration Best Practices

8. Performance Optimization

8.1 Understanding Query Performance
8.2 Reducing Data Load Time
8.3 Efficient Use of Filters
8.4 Handling Large Data Sets
8.5 Monitoring Query Efficiency

9. Governance and Best Practices

9.1 Ensuring Data Consistency
9.2 Avoiding Common Analysis Errors
9.3 Following Naming Standards
9.4 Managing Access and Permissions
9.5 Maintaining Data Accuracy

10. Real-World Use Cases and Projects

10.1 Exploratory Data Analysis Scenarios
10.2 Business Insights Generation
10.3 KPI Analysis and Tracking
10.4 End-to-End Ad-hoc Analysis
10.5 Capstone Project

Conclusion

In conclusion, this training builds strong data exploration skills. Moreover, it improves ad-hoc analysis techniques. Therefore, learners can analyze data quickly and effectively. As a result, they can generate meaningful insights and support better decisions.