Looker Tutorial

What is Looker Tutorial?

Looker Tutorial is a modern business intelligence (BI) and data analytics platform that helps organizations explore, visualize, and share real-time data insights. Now part of Google Cloud, Looker empowers users to make data-driven decisions with custom dashboards and embedded analytics.

Key Features of Looker

• Powerful data modeling using LookML

• Real-time analytics connected to your database

• Fully customizable dashboards and reports

• Role-based data access and governance

• Embedded analytics for SaaS apps

• Integration with Google BigQuery, Snowflake, Redshift, etc.

Getting Started with Looker

Step 1: Access Looker

• Looker is a cloud-based tool. Access it via your organization’s Looker instance or request a demo from cloud.google.com/looker.

Looker Core Concepts

How Looker Works

1. Connects directly to your database (no data extracts)

2. Models your data with LookML

3. Allows users to Explore data with a drag-and-drop interface

4. Generates SQL behind the scenes

5. Displays results via tables, charts, maps, or dashboards

Creating Your First Explore

1. Click Explore from the top navigation.

2. Select a model (e.g., Orders, Customers).

3. Drag fields to build your query.

4. Apply filters, aggregations, and pivot options.

5. Click Run to execute the SQL query.

6. Save the output as a Look or export the data.

Building Dashboards in Looker

1. Go to Dashboards > New Dashboard

2. Add tiles:

   • From saved Looks

   • From new Explores

3. Arrange tiles using drag-and-drop

4. Apply dashboard-level filters

5. Share with specific users or schedule deliveries

Access Controls in Looker

• Use User Roles and Permissions to restrict data access.

• Apply Row-Level Security (RLS) using LookML filters.

• Embed dashboards with secure access via API tokens or OAuth.

Introduction to LookML (Looker Modeling Language)

LookML is the syntax that defines how data is transformed and made available in Looker.

Example: view for Orders Table

Example: explore in Model File

Filters and Parameters in Looker

• Add filters to Explores and dashboards using UI or LookML

• Example: Filter on order date

Looker Use Cases

• Marketing analytics: Track campaigns, leads, and conversion rates

• Sales dashboards: Analyze pipeline, revenue, and quotas

• Finance: Forecasting and expense tracking

• Product analytics: Feature usage, churn, retention

• Embedded analytics: Custom reporting for your SaaS users

Integrations and APIs

• Native integrations: BigQuery, Snowflake, Redshift, MySQL, PostgreSQL

• Webhooks and schedule deliveries to Slack, Gmail, S3

• Looker API for:

  • Creating Looks

  • Running queries

  • Managing users and permissions

Looker Visualization Types

• Tables

• Bar, Line, Pie Charts

• Heatmaps

• Geographic Maps

• Treemaps

• Single value (KPI tiles)

Looker Best Practices

1. Centralize logic in LookML – avoid duplicated logic in dashboards.

2. Use naming conventions for views, dimensions, and measures.

3. Apply version control using Git integration.

4. Test changes in development mode before deploying.

5. Use persistent derived tables (PDTs) to optimize performance.

Conclusion

Looker is a powerful BI tool that combines the flexibility of SQL with the scalability of the cloud. By mastering LookML and dashboard design, you can turn raw data into actionable insights across your organization.

Whether you’re just starting out or preparing for a career in data analytics, Looker is a valuable tool to have in your stack.