¶ Looker
¶ Tools
Looker is one of those tools that you don’t fully grasp until you actually start using it with real data—messy data, ambitious data, data that changes direction faster than your plans do. At first glance, Looker seems like another business intelligence platform: dashboards, charts, filters, drill-downs. But the more time you spend with it, the more you realize that it isn’t designed to be just another analytics layer. Looker is built around a philosophy that changes the relationship between teams and information. It treats data not as a static artifact you pull into reports, but as a living system that should be modeled, governed, shared, and explored in a way that empowers every single person in an organization.
This course—one hundred articles dedicated to Looker—is not just a technical walkthrough. It is a journey into the way Looker reshapes data culture. It’s about learning how to think more clearly about metrics, how to design clean semantic layers, how to empower teams through thoughtful modeling, and how to turn raw tables into insights that actually drive action. Looker is not simply a visualization tool; it’s the backbone of a modern data environment. And when you understand it deeply, you start to see why so many high-performing teams rely on it to guide decisions, communicate performance, and unify their understanding of the business.
Before diving into the mechanics, it's helpful to understand Looker’s fundamental idea: one source of truth. Not “kind of” one. Not “close enough.” A real, governed, consistent truth about your data—definitions that don’t break from team to team, metrics that don’t mutate in different dashboards, and logic that doesn’t get copied, pasted, and subtly altered. Anyone who has ever worked in analytics knows how easy it is for definitions to drift over time. Monthly recurring revenue, active users, churn, conversion rates—every department might calculate them differently. As a result, meetings become debates over whose spreadsheet is more correct rather than discussions about what actually matters.
Looker’s answer to this problem is LookML, its modeling language. LookML seems intimidating at first, but once you understand it, you realize how elegant it truly is. Instead of burying business logic inside dozens of dashboards or hiding SQL code in scattered reports, you define your business rules once—in a clean, transparent, version-controlled layer. And from that foundation, every visualization, every query, every report inherits the same consistent logic. When a metric changes, you update it once and the entire organization reflects that change. Looker, in essence, creates an environment where logic is centralized and interpretation is decentralized. People throughout the company can explore data freely without breaking definitions.
This course starts with that core idea. The early articles will introduce you to the mindset behind modeling. Not just how to write LookML, but how to think in LookML—how to design dimensions and measures that communicate clearly, how to structure explores that encourage curiosity rather than overwhelm users, and how to build semantic layers that anticipate growth rather than reacting to problems later. Good LookML is not just clean code; it’s a blueprint for how your organization understands itself.
As we continue, the course will explore how Looker transforms the analytics workflow. Most BI tools emphasize dashboards as the centerpiece. Dashboards matter, of course, but Looker pushes you toward a richer way of working: ad-hoc exploration. When you organize your model well, non-technical users can answer complex questions without writing SQL. They can pivot data, apply filters, build visualizations, and even create temporary derived tables directly from the explore environment. Looker doesn’t trap people inside reports someone else built for them; it invites them to think, dig, and discover on their own.
This freedom is powerful—but it only works when the underlying model is solid. As you progress through the course, you’ll learn how to build models that anticipate user behavior. You’ll explore concepts like join logic, relationship management, drill paths, dimension groups, aliases, and aggregate tables. You’ll learn how to prevent fan-outs, avoid double-counting, and structure your model so that even inexperienced analysts don’t generate misleading results. LookML is part language, part craft, part architecture. And this course aims to help you master all three.
Then there's the question of governance. Data governance might sound like a dry subject, something buried in documentation that only a few people care about. But in Looker, governance is woven into the everyday experience. When you organize folders, manage permissions, create groups, define user roles, and structure content thoughtfully, you make data safer, cleaner, and less intimidating. This course will explore how Looker handles access control, how you can segment data by department or geography, and how to balance openness with protection. Good governance helps people feel confident—not just technically, but culturally.
A major theme of the course will be collaboration. Looker encourages teams to share—not just final dashboards, but ideas, query links, visualizations, and insights in progress. You can send someone a link to an explore, and they immediately land in the exact state you left it: filters, pivots, fields, everything. It’s a small detail that changes the way teams work. Instead of sending screenshots or exporting spreadsheets, you share a living query. The recipient can adjust it, refine it, dig deeper. Looker turns analytics into a conversation rather than a static deliverable.
We’ll also explore Looker’s integration with modern data stacks. Snowflake, BigQuery, Redshift, Databricks—Looker sits comfortably on top of these platforms, pushing down queries efficiently and taking advantage of the raw power of your warehouse. Unlike tools that extract data or maintain their own internal engine, Looker sends SQL directly to your source, meaning your warehouse remains the single source of truth. Throughout the course, you'll learn how Looker interacts with warehouse configurations, how caching works, how persistent derived tables fit into performance strategies, and how to align Looker’s modeling philosophy with the strengths of your underlying database.
Another important thread is visualization. Looker’s visual layer is intentionally simple, focusing on clarity over flashy gimmicks. But simplicity doesn’t mean limitation. You’ll explore how to design visualizations that communicate effectively, how to choose the right chart type for the right question, how to customize colors and themes, and how to build dashboards that feel like coherent narratives rather than chaotic collections of charts. Dashboards aren’t just artifacts—they’re storytelling tools. And Looker rewards thoughtful storytelling.
Later sections of the course will focus on advanced LookML patterns. You’ll learn about parameterization, templated filters, liquid variables, refined joins, user attributes, time-based modeling, and reusable snippets. You’ll see how to build flexible models that support multiple levels of analysis—global metrics, regional breakdowns, customer segments, product lines, channel behavior. As your model grows, you’ll learn how to maintain its clarity, how to document it so that others understand your decisions, and how to structure your repository for long-term sustainability.
But the course won’t end with modeling alone. Looker is also a platform for automation and action. With scheduled reports, alerts, webhooks, integrations, and Looker Blocks, you can push insights into other systems—Slack, email, CRMs, ticketing platforms, operational tools. Data doesn’t have to live passively inside dashboards. Looker helps data move into the daily rhythm of your organization. Later articles will show you how to design alerts that actually matter, how to push insights to the right teams at the right moments, and how to build processes that turn analysis into action.
Towards the final portion of the series, we’ll explore how Looker supports data culture at scale. Looker isn’t just a tool—it becomes part of the identity of a data-driven company. It fosters a shared vocabulary, a shared understanding of key metrics, and a shared approach to exploring information. You’ll learn how to onboard new users, showcase best practices, organize content libraries, and build internal communities around Looker. You’ll see how analysts evolve from building dashboards to shaping the entire environment. And you’ll understand how a single modeling layer can create alignment between engineering, analytics, product, operations, finance, and leadership.
By the time you finish the last article in this course, Looker will no longer feel like a BI platform. It will feel like a companion for understanding your organization. You’ll know how to build semantic layers that scale gracefully, how to empower users without overwhelming them, how to design dashboards that guide decision-making, and how to maintain a clean, stable analytics environment long after the first version of your model is published.
Most importantly, you’ll think differently about data. Looker encourages a quieter, more thoughtful approach—one where clarity matters more than complexity, where definitions matter more than dashboards, where modeling is an act of making meaning rather than simply moving fields around. It reminds you that good analytics isn’t just about numbers; it’s about communication, governance, curiosity, structure, and trust.
This course is an invitation to explore Looker with patience and depth. Whether you're an analyst, a data engineer, a product leader, or someone stepping into analytics for the first time, the upcoming articles will help you grow into a more confident and more strategic data thinker.
Let’s begin.
¶ Looker
¶ Beginner Level:
1. Introduction to Looker: What It Is and How It Works
2. Setting Up Your Looker Account and Workspace
3. Understanding Looker’s Interface: Key Components
4. How to Navigate the Looker Dashboard and Explore Views
5. Understanding Looker’s Data Model: Connections and Connections Setup
6. Introduction to Looker’s Data Connections and Data Sources
7. How to Create and Share a Looker Report
8. How to Use Looker’s Explore Feature for Data Discovery
9. Understanding Dimensions and Measures in Looker
10. Introduction to Creating Visualizations in Looker
11. How to Use Looker’s Filters for Data Exploration
12. Saving and Sharing Looks and Dashboards in Looker
13. How to Add and Modify Data in Looker
14. Understanding Looker’s Data Security and Permissions
15. How to Customize Your Looker Dashboards for Better Insights
16. How to Work with Tables and Charts in Looker Visualizations
17. How to Export Data from Looker to CSV and Excel
18. Understanding Looker’s Time Series Visualizations
19. Introduction to Looker’s Search and Filter Capabilities
20. How to Use Looker’s Field Picker to Select Dimensions and Measures
21. Introduction to Data Exploration Using Looker’s UI
22. How to Create and Share Reports in Looker
23. Understanding Looker’s Basic LookML Language
24. Introduction to Looker’s API for Data Access
25. How to Connect Looker to Your Data Warehouse
26. How to Create a Simple Bar Chart in Looker
27. Getting Started with Looker’s Built-in Calculations
28. Introduction to Pivoting Data in Looker
29. Understanding Looker’s SQL Runner for Querying Data
30. How to Set Up and Use Looker Alerts and Notifications
¶ Intermediate Level:
31. Understanding LookML: Looker’s Modeling Language
32. Creating and Managing LookML Projects in Looker
33. Building and Customizing Looker Dashboards for Real-Time Insights
34. Using Looker’s Join Operations for Data Merging
35. How to Create Advanced Visualizations in Looker
36. Using Derived Tables in Looker to Enhance Reports
37. How to Work with Parameters in Looker for Dynamic Queries
38. Creating and Using Custom Calculations in Looker
39. Using Filters and Conditional Logic in Looker to Refine Reports
40. Introduction to Looker’s Scheduling and Emailing Features
41. Understanding Looker’s Access Control and User Permissions
42. How to Create and Organize Data Models in Looker
43. Using Advanced Data Exploration Techniques in Looker
44. How to Customize Looker Visualizations with Custom Themes
45. Creating Multi-Table Dashboards in Looker
46. How to Share and Embed Looker Dashboards for External Users
47. How to Use Looker’s Drill-Down Feature for Deeper Insights
48. Understanding Time Filters and Date Handling in Looker
49. Exploring the Use of Looker’s Calculated Fields in Reports
50. How to Create and Use Multiple Data Sources in Looker
51. Managing Data Quality and Integrity in Looker Reports
52. How to Build and Share Complex Reports in Looker
53. Using Looker’s User Attributes to Personalize Reports
54. How to Integrate Looker with Google Analytics for Web Reporting
55. Introduction to Looker’s Geospatial Visualizations
56. How to Use Looker’s API for Automating Tasks
57. How to Optimize Looker Queries for Better Performance
58. Creating and Using Cohorts in Looker for Group Analysis
59. How to Create Sub-queries and Nested Queries in Looker
60. Introduction to Scheduled Data Extracts in Looker
¶ Advanced Level:
61. Advanced LookML: Creating Reusable Looker Models
62. Building Complex Data Models Using Looker’s LookML
63. How to Use Looker’s Persistent Derived Tables for Performance Gains
64. Managing Multiple Looker Projects and Dependencies
65. Building Custom Data Visualizations Using Looker’s Visualization API
66. Advanced Filters and Contextual Data in Looker Reports
67. How to Build and Manage Complex Dashboards with Multiple Filters
68. Using Advanced Calculations for Business Metrics in Looker
69. How to Use Looker’s Window Functions for Advanced Analytics
70. Understanding Looker’s Advanced Join Types and Their Impact
71. Creating Custom Data Aggregations in Looker
72. How to Design Looker Dashboards for Large Data Sets
73. Leveraging Looker’s Integration with Data Warehouses (BigQuery, Snowflake)
74. How to Use Looker’s Data Blending for Cross-Domain Insights
75. Implementing Machine Learning Models with Looker Data
76. Managing Looker Permissions and Roles in Complex Environments
77. How to Build Real-Time Dashboards with Looker
78. Advanced Techniques for Using the Looker API for Data Pipelines
79. Integrating Looker with External Business Intelligence Tools (Tableau, Power BI)
80. How to Automate Data Delivery and Reporting Using Looker’s Scheduling
81. Building and Managing Custom Looker Apps with JavaScript and API
82. Using Advanced Time Series Analysis in Looker
83. How to Implement Custom LookML Extensions in Looker
84. Looker’s Best Practices for Performance Optimization
85. Creating Complex Metrics and KPIs in Looker
86. How to Use Looker for Data Governance and Auditing
87. Integrating Looker with CRM and Marketing Platforms for Cross-Platform Insights
88. How to Use Advanced Visualizations (Sankey Diagrams, Heatmaps) in Looker
89. Mastering Looker’s Advanced Data Exploration for Data Scientists
90. How to Apply Statistical Methods in Looker’s Data Models
91. Implementing Advanced User Permissions and Access Control in Looker
92. Managing and Maintaining Multiple Looker Instances
93. How to Work with External Data Sets Using Looker’s Data Connections
94. Integrating Looker with Business Intelligence Tools for Cross-Platform Analysis
95. Leveraging Looker’s Customizable Dashboards for Business Insights
96. Understanding Looker’s Advanced ETL Capabilities for Data Pipelines
97. Creating Custom Alerts and Triggers in Looker for Real-Time Insights
98. How to Design and Implement Cross-Company Dashboards in Looker
99. How to Conduct A/B Testing Using Looker’s Reporting Features
100. Managing Looker’s Advanced Customizations for Enterprise-Level Solutions