Course 1: Looker Foundations

Week 1: Introduction to Looker Platform

Introduction: The Looker Platform is a cloud-based business intelligence (BI) tool designed for data exploration, visualization, and sharing to support data-driven decisions. This week introduces the Looker interface, key features, and basic navigation, with a hands-on example emphasizing how to use Looker to access and explore a sample dataset.

Learning Objectives: By the end of this week, you will be able to:

Understand Looker’s purpose and core functionalities.
Navigate the Looker user interface (Home, Explore, Dashboards, Looks).
Access and interact with a sample dataset in Looker.
Identify key components like dimensions, measures, and folders.
Set up a Looker account and personalize basic settings.

Scope: This week covers the Looker Platform’s overview, interface navigation, and initial data interaction using the Explore page. It sets the foundation for subsequent weeks on visualizations, dashboards, and advanced features, focusing on practical usage for beginners.

Background Information: Looker is a BI and analytics platform that connects to SQL databases, enabling users to explore data, create visualizations, and share insights via a web-based interface. Key aspects include:

Purpose: Facilitates self-service BI for non-technical users to query and visualize data. Supports data governance with a centralized data model (LookML).
Key Features: Explore: Query builder for ad-hoc data analysis. Looks: Saved reports or visualizations. Dashboards: Collections of visualizations for comprehensive insights. LookML: Looker’s modeling language for defining data relationships (introduced in Week 9).

Applications: Analyze sales performance across regions. Monitor marketing campaign metrics.

Challenges: Understanding Looker’s terminology (e.g., dimensions vs. measures). Navigating large datasets without prior BI experience.

Hands-On Example: Scenario: You’re a sales analyst tasked with exploring a sample sales dataset in Looker to understand its interface and data structure. You’ll log in, navigate the platform, and interact with the dataset to view sales metrics.

Prerequisites: Computer with Windows 10/11 or macOS 12+ (8 GB RAM, 20 GB free disk space). Looker account (sign up at https://looker.google.com/try-looker). Basic computer skills and a sample Excel file.
Step 1: Set Up a Looker Account: Access Looker, sign up, and log in. Explore the Home page.
Step 2: Navigate the Interface: Familiarize with Home, Explore, and other tabs.
Step 3: Access a Sample Dataset: Connect to a sales dataset and build a basic query.
Step 4: Interact and Save: Visualize data and save as a Look.
Step 5: Save and Export: Save the Look and export results.

Interpretation: This hands-on example demonstrates how to use Looker’s interface to access and explore a sales dataset, create a basic query, and save a Look. By navigating the platform and testing interactivity, you gain foundational skills for data exploration in Week 2.

Supplemental Information:

Looker Introduction: https://cloud.google.com/looker/docs/intro
Explore Interface: https://cloud.google.com/looker/docs/exploring-data
Getting Started: https://looker.google.com/try-looker

Discussion Points:

How does Looker’s web-based interface aid accessibility?
Why are dimensions and measures critical for querying?
What challenges might arise for new users navigating Looker?
How does Looker’s self-service BI empower non-technical users?
How can exploring sample datasets build confidence?

Week 2: Exploring Data in Looker

Introduction: Exploring data in Looker allows users to query datasets, select fields, and generate insights using the Explore interface. This week focuses on how to use Looker’s Explore page to build queries, apply basic filters, and interpret results, with a hands-on example emphasizing practical steps to analyze a sales dataset.

Learning Objectives: By the end of this week, you will be able to:

Use the Explore page to select dimensions and measures for queries.
Apply basic filters to refine query results.
Interpret query results in tables and preview visualizations.
Save queries as Looks for reuse.
Validate query accuracy and troubleshoot common issues.

Scope: This week covers querying data in Looker’s Explore interface, including selecting fields, applying filters, and saving results as Looks. It builds on Week 1’s introduction to the Looker Platform, focusing on practical data exploration techniques to prepare for visualizations in Week 3.

Background Information: Looker’s Explore page is the core tool for data exploration:

Explore Interface: Data Pane: Lists dimensions (categorical fields, e.g., Product) and measures (numeric metrics, e.g., Sum of Sales). Query Builder: Add fields, filters, and sorts to construct queries. Results Table: Displays query output. Visualization Preview: Quick charts to visualize results.
Key Actions: Select Fields: Drag or click dimensions/measures to build queries. Filter: Limit data (e.g., Region = North). Sort: Order results by fields. Save as Look: Store queries for reuse or dashboard inclusion.

Applications: Analyze sales by product to identify top performers. Filter data to focus on specific time periods or regions.

Challenges: Choosing appropriate dimensions and measures for analysis. Applying filters without excluding critical data. Understanding aggregation behavior (e.g., SUM vs. COUNT).

Hands-On Example: Scenario: You’re a sales analyst exploring a sales dataset in Looker to understand product performance and regional sales trends. You’ll use the Explore page to build queries, apply filters, and save results as a Look.

Prerequisites: Computer with Windows 10/11 or macOS 12+ (8 GB RAM, 20 GB free disk space). Looker account. Sample dataset loaded.
Step 1: Access the Explore Page: Log in and select the “Sales” model.
Step 2: Build a Query: Select fields, apply filters, and sort results.
Step 3: Visualize and Save: Preview charts and save as a Look.
Step 4: Test and Export: Validate the Look and export results.
Step 5: Manage the Look: Organize and share if needed.

Interpretation: This hands-on example demonstrates how to use Looker’s Explore page to query a sales dataset, apply filters, and save results as Looks. By building and validating queries, you develop practical skills for data exploration, preparing for creating visualizations in Week 3.

Supplemental Information:

Exploring Data: https://cloud.google.com/looker/docs/exploring-data
Filters in Looker: https://cloud.google.com/looker/docs/filters
Saving Looks: https://cloud.google.com/looker/docs/saving-looks

Discussion Points:

How does the Explore page simplify data querying?
Why are filters essential for targeted analysis?
What challenges arise when selecting dimensions and measures?
How do saved Looks improve efficiency?
How can preview visualizations guide query refinement?

Week 3: Creating Basic Visualizations

Introduction: Visualizations in Looker transform queried data into charts and graphs, making insights accessible and actionable. This week focuses on how to use Looker’s Visualization tab to create basic visualizations (e.g., bar, line, pie charts) in the Explore interface, with a hands-on example emphasizing practical steps to visualize sales data.

Learning Objectives: By the end of this week, you will be able to:

Create basic visualizations (bar, line, pie, table) in Looker’s Explore page.
Customize visualization settings (e.g., colors, labels, axes).
Save visualizations as Looks for reuse.
Interpret and validate visualization results for accuracy.
Troubleshoot common visualization issues.

Scope: This week covers creating and customizing visualizations in Looker’s Explore interface, building on Week 1’s platform introduction and Week 2’s data exploration. It focuses on practical visualization techniques to prepare for building dashboards in Week 4.

Background Information: Looker’s Visualization tab enables users to represent query results graphically:

Visualization Types: Bar: Compare categories (e.g., sales by product). Line: Show trends over time (e.g., sales by month). Pie: Display proportions (e.g., sales by region). Table: Present detailed data in tabular form.
Customization Options: Edit Visualization: Adjust colors, labels, titles, and axes. Series: Configure data series for multi-dimensional charts. Formatting: Set number formats (e.g., currency) and font styles.

Applications: Visualize product sales to identify top performers. Track monthly sales trends for forecasting.

Challenges: Choosing the right chart type for the data. Avoiding cluttered visuals with too many dimensions. Ensuring accurate data representation (e.g., correct aggregations).

Hands-On Example: Scenario: You’re a sales analyst tasked with creating visualizations to analyze sales performance by product, region, and month. You’ll use Looker’s Explore page to build bar, pie, and line charts, customize them, and save them as Looks.

Prerequisites: Computer with Windows 10/11 or macOS 12+ (8 GB RAM, 20 GB free disk space). Looker account. Sample dataset loaded.
Step 1: Access the Explore Page: Log in and select the “Sales” model.
Step 2: Build Queries: Create queries for sales by product, region, and month.
Step 3: Create and Customize Visualizations: Apply bar, pie, and line charts with custom settings.
Step 4: Save and Export: Save as Looks and export results.
Step 5: Validate: Test and troubleshoot visualizations.

Interpretation: This hands-on example demonstrates how to use Looker’s Explore page to create and customize bar, pie, and line visualizations for a sales dataset. By validating and exporting results, you develop practical skills for visualizing data, preparing for dashboard creation in Week 4.

Supplemental Information:

Visualizations in Looker: https://cloud.google.com/looker/docs/creating-visualizations
Chart Types: https://cloud.google.com/looker/docs/chart-types
Customizing Visualizations: https://cloud.google.com/looker/docs/customizing-visualizations

Discussion Points:

How do visualizations enhance data interpretation?
Why is choosing the right chart type critical?
What challenges arise when customizing visualizations?
How do saved Looks streamline analysis?
How can visualizations be made clear and uncluttered?

Week 4: Building & Sharing Dashboards

Introduction: Dashboards in Looker combine multiple visualizations (Looks) into a single, interactive view to provide comprehensive insights. This week focuses on how to use Looker to create dashboards, add Looks, apply filters, and share dashboards with others, with a hands-on example emphasizing practical steps to build and share a sales dashboard.

Learning Objectives: By the end of this week, you will be able to:

Create a dashboard in Looker and add existing Looks.
Customize dashboard layout and apply dashboard-level filters.
Share dashboards via links or Looker’s sharing options.
Validate dashboard functionality and interactivity.
Troubleshoot common dashboard issues.

Scope: This week covers creating, customizing, and sharing dashboards in Looker, building on Week 1’s platform introduction, Week 2’s data exploration, and Week 3’s visualizations. It focuses on practical dashboard creation to prepare for understanding Looks and the user interface in Week 5.

Background Information: Dashboards in Looker are powerful tools for presenting insights:

Purpose: Aggregate multiple Looks (visualizations or tables) into one view. Enable interactive analysis with filters and clickable elements.
Key Features: Dashboard Creation: Via Dashboards > Create Dashboard. Tiles: Individual Looks or text elements added to the dashboard. Filters: Apply to all tiles for consistent interactivity. Sharing: Share via URL, Looker’s sharing settings, or embedded links.

Applications: Build a sales dashboard for executives to monitor performance. Share marketing insights with stakeholders.

Challenges: Ensuring consistent data across tiles. Optimizing layout for clarity and usability. Managing access for shared dashboards.

Hands-On Example: Scenario: You’re a sales analyst tasked with creating a sales dashboard in Looker to present product, region, and monthly sales data. You’ll combine existing Looks, customize the layout, add filters, and share the dashboard with your team.

Prerequisites: Computer with Windows 10/11 or macOS 12+ (8 GB RAM, 20 GB free disk space). Looker account. Existing Looks from Week 3.
Step 1: Access the Dashboard Editor: Log in and create a new dashboard.
Step 2: Add Looks: Include previously created Looks.
Step 3: Customize and Share: Apply filters and share the dashboard.
Step 4: Test and Export: Validate and export the dashboard.
Step 5: Manage Sharing: Handle permissions and feedback.

Interpretation: This hands-on example demonstrates how to use Looker to create a sales dashboard by combining Looks, customizing the layout, and adding interactive filters. By sharing and validating the dashboard, you develop practical skills for presenting insights, preparing for deeper exploration of Looks and the user interface in Week 5.

Supplemental Information:

Creating Dashboards: https://cloud.google.com/looker/docs/creating-dashboards
Dashboard Filters: https://cloud.google.com/looker/docs/dashboard-filters
Sharing Dashboards: https://cloud.google.com/looker/docs/sharing-content

Discussion Points:

How do dashboards enhance data communication?
Why are dashboard filters critical for interactivity?
What challenges arise when arranging multiple visualizations?
How does sharing dashboards improve team collaboration?
How can dashboard layouts be optimized for usability?

Week 5: Understanding Looks & User Interface

Introduction: Looks in Looker are saved queries or visualizations that enable reusable data exploration and reporting. The user interface (UI) provides intuitive navigation and interaction with these elements. This week focuses on how to use Looker to create, manage, and interact with Looks while exploring the UI’s core components, with a hands-on example emphasizing practical steps to work with a sales dataset.

Learning Objectives: By the end of this week, you will be able to:

Create and modify Looks in Looker’s Explore page.
Navigate and utilize key UI components (Home, Explore, Dashboards, Looks, Folders).
Organize Looks in folders and manage their settings.
Interact with Looks (e.g., edit, duplicate, share).
Validate Look functionality and troubleshoot UI-related issues.

Scope: This week covers the creation, management, and interaction with Looks, alongside a deeper exploration of Looker’s UI, building on Week 1’s platform introduction, Week 2’s data exploration, Week 3’s visualizations, and Week 4’s dashboards. It focuses on practical usage to prepare for filtering and sorting data in Week 6.

Background Information: Looks and the Looker UI are central to efficient data analysis:

Looks: Saved queries or visualizations (tables, charts) created in the Explore page. Stored in Personal or Shared Folders for reuse in dashboards or standalone reports. Editable, duplicatable, and shareable with team members.
User Interface Components: Home: Central hub for recent activity, favorites, and content access. Explore: Query-building interface for creating Looks. Dashboards: View for aggregated Looks (covered in Week 4). Looks: Dedicated section to browse and manage saved Looks. Folders: Organize content (Personal, Shared, or custom).

Applications: Create a Look to track sales by product for quick reference. Use the UI to navigate between Looks and dashboards efficiently.

Challenges: Keeping folders organized with multiple Looks. Understanding Look permissions for sharing. Navigating the UI effectively for new users.

Hands-On Example: Scenario: You’re a sales analyst tasked with creating and managing Looks to analyze sales data by product and region, while exploring Looker’s UI to organize and interact with them. You’ll create Looks, organize them in folders, update and duplicate them, and validate their functionality.

Prerequisites: Computer with Windows 10/11 or macOS 12+ (8 GB RAM, 20 GB free disk space). Looker account. Sample dataset loaded.
Step 1: Access the Explore Page: Log in and select the “Sales” model.
Step 2: Create and Save Looks: Build and save Looks for sales analysis.
Step 3: Organize and Manage: Move, update, and duplicate Looks.
Step 4: Interact with UI: Use Home, Folders, and other components.
Step 5: Validate and Export: Test and export Looks.

Interpretation: This hands-on example demonstrates how to use Looker to create and manage Looks for a sales dataset while navigating the UI to organize, edit, and share them. By validating functionality, you build practical skills for working with Looks, preparing for collaboration features in Week 6.

Supplemental Information:

Looks Overview: https://cloud.google.com/looker/docs/saving-looks
Looker UI: https://cloud.google.com/looker/docs/user-interface
Managing Content: https://cloud.google.com/looker/docs/managing-content

Discussion Points:

How do Looks improve data reusability?
Why is folder organization critical for teams?
What challenges arise when updating shared Looks?
How do permissions impact Look management?
How can duplicating Looks support experimentation?

Week 6: Filtering & Sorting Data in Looker

Introduction: Filtering and sorting data in Looker allow users to refine queries and organize results for targeted analysis. This week focuses on how to use Looker’s Explore page to apply advanced filters and sorting options, with a hands-on example emphasizing practical steps to analyze a sales dataset by applying filters and sorting to uncover insights.

Learning Objectives: By the end of this week, you will be able to:

Apply advanced filters (e.g., multiple conditions, date ranges) in the Explore page.
Sort query results by multiple fields and directions.
Combine filtering and sorting to refine visualizations.
Save filtered and sorted queries as Looks.
Validate filter and sort accuracy and troubleshoot issues.

Scope: This week covers advanced filtering and sorting techniques in Looker’s Explore interface, building on Week 1’s platform introduction, Week 2’s data exploration, Week 3’s visualizations, Week 4’s dashboards, and Week 5’s Looks and UI. It focuses on practical data refinement to prepare for saving and managing Looks in Week 7.

Background Information: Filtering and sorting are essential for precise data analysis in Looker:

Filtering: Restricts data in queries using conditions (e.g., Region = North, Sales > 1000). Types: Basic: Single condition. Advanced: Multiple conditions, date ranges, or custom expressions.
Sorting: Orders results by one or more fields (e.g., Sales descending, Date ascending).

Applications: Filter sales data to focus on high-value transactions. Sort products by sales to identify top performers.

Challenges: Avoiding overly restrictive filters that exclude relevant data. Ensuring sorting aligns with analysis goals. Managing complex filter combinations for accuracy.

Hands-On Example: Scenario: You’re a sales analyst tasked with analyzing a sales dataset to identify top-performing products and regional trends. You’ll use Looker’s Explore page to apply advanced filters and sorting, create visualizations, and save results as Looks.

Prerequisites: Computer with Windows 10/11 or macOS 12+ (8 GB RAM, 20 GB free disk space). Looker account. Sample dataset loaded.
Step 1: Access the Explore Page: Log in and select the “Sales” model.
Step 2: Apply Filters and Sorting: Build queries with advanced filters and multi-sorting.
Step 3: Visualize and Save: Create and save Looks with filtered and sorted data.
Step 4: Test and Export: Validate and export results.
Step 5: Manage Looks: Organize and share if needed.

Interpretation: This hands-on example demonstrates how to use Looker’s Explore page to apply advanced filters and sorting to a sales dataset, creating targeted visualizations and Looks. By validating results, you develop practical skills for data refinement, preparing for saving and managing Looks in Week 7.

Supplemental Information:

Filters in Looker: https://cloud.google.com/looker/docs/filters
Sorting Data: https://cloud.google.com/looker/docs/sorting-data
Advanced Filtering: https://cloud.google.com/looker/docs/advanced-filters

Discussion Points:

How do filters enhance targeted analysis?
Why is multi-sorting useful for complex datasets?
What challenges arise with advanced filter combinations?
How do filtered Looks improve dashboard interactivity?
How can sorting align with analysis objectives?

Week 7: Saving & Managing Looks

Introduction: Saving and managing Looks in Looker ensures that queries and visualizations are reusable, organized, and accessible for analysis. This week focuses on how to use Looker to save Looks with specific configurations, manage them in folders, and update or delete them as needed, with a hands-on example emphasizing practical steps to organize and maintain a set of sales-related Looks.

Learning Objectives: By the end of this week, you will be able to:

Save Looks with customized queries, filters, and visualizations.
Organize Looks in Personal and Shared Folders for efficient access.
Update, duplicate, and delete Looks to maintain relevance.
Manage Look permissions and visibility settings.
Validate Look integrity and troubleshoot management issues.

Scope: This week covers saving, organizing, and managing Looks in Looker, building on Week 1’s platform introduction, Week 2’s data exploration, Week 3’s visualizations, Week 4’s dashboards, Week 5’s Looks and UI, and Week 6’s filtering and sorting. It focuses on practical Look management to prepare for collaboration features in Week 8.

Background Information: Looks are the backbone of Looker’s reusable reporting:

Saving Looks: Created in Explore by saving queries or visualizations. Stored in Personal Folder (private) or Shared Folders (team-accessible). Include query details (fields, filters, sorts) and visualization settings.
Managing Looks: Organize: Move to folders or subfolders for clarity. Update: Edit queries, filters, or visualizations and overwrite. Duplicate: Create copies for experimentation without altering originals. Delete: Remove outdated Looks to reduce clutter. Permissions: Control access via folder settings (admin-dependent).

Applications: Save a sales performance Look for weekly reviews. Organize Looks by project (e.g., Sales, Marketing) in folders.

Challenges: Avoiding clutter with too many Looks. Ensuring proper permissions for shared Looks. Tracking updates to prevent overwriting critical versions.

Hands-On Example: Scenario: You’re a sales analyst tasked with creating and managing a set of Looks to analyze sales data by product and region. You’ll save Looks with specific configurations, organize them in folders, update and duplicate them, and validate their functionality.

Prerequisites: Computer with Windows 10/11 or macOS 12+ (8 GB RAM, 20 GB free disk space). Looker account. Sample dataset loaded.
Step 1: Access the Explore Page: Log in and select the “Sales” model.
Step 2: Create and Save Looks: Build and save Looks for sales analysis.
Step 3: Organize and Manage: Move, update, and duplicate Looks.
Step 4: Interact with UI: Use Home, Folders, and other components.
Step 5: Validate and Export: Test and export Looks.

Interpretation: This hands-on example demonstrates how to use Looker to save and manage Looks for a sales dataset, organizing them in folders, updating configurations, and ensuring accessibility. By validating functionality, you develop practical skills for Look management, preparing for collaboration features in Week 8.

Supplemental Information:

Saving Looks: https://cloud.google.com/looker/docs/saving-looks
Managing Content: https://cloud.google.com/looker/docs/managing-content
Folder Management: https://cloud.google.com/looker/docs/folders

Discussion Points:

How does saving Looks improve analysis efficiency?
Why is folder organization critical for teams?
What challenges arise when updating shared Looks?
How do permissions impact Look management?
How can duplicating Looks support experimentation?

Week 8: Collaboration Features in Looker

Introduction: Collaboration features in Looker enable teams to share, comment, and work together on Looks and dashboards, fostering data-driven decision-making. This week focuses on how to use Looker to share content, manage permissions, and utilize collaboration tools like comments and subscriptions, with a hands-on example emphasizing practical steps to collaborate on a sales dashboard.

Learning Objectives: By the end of this week, you will be able to:

Share Looks and dashboards via URLs or folder permissions.
Use Looker’s commenting feature to provide feedback on content.
Subscribe to Looks and dashboards for regular updates.
Manage access and visibility settings for collaborative content.
Validate collaboration features and troubleshoot sharing issues.

Scope: This week covers Looker’s collaboration tools, including sharing, commenting, subscriptions, and permission management, building on Week 1’s platform introduction, Week 2’s data exploration, Week 3’s visualizations, Week 4’s dashboards, Week 5’s Looks and UI, Week 6’s filtering and sorting, and Week 7’s Look management. It focuses on practical collaboration to prepare for the data dictionary in Week 9.

Background Information: Looker’s collaboration features streamline teamwork:

Sharing: Looks and Dashboards: Share via public URLs (admin-enabled), internal URLs, or folder permissions. Folders: Move content to Shared Folders for team access.
Commenting: Available on dashboards and Looks for feedback (requires permissions). Comments are visible to users with access to the content.
Subscriptions: Schedule email delivery of Looks or dashboards (e.g., daily, weekly). Configured via Schedule options in content settings.
Permissions: Folder Permissions: Control who can view or edit (admin-managed). Content Access: Public, group-based, or user-specific access levels.

Applications: Share a sales dashboard with stakeholders for review. Comment on a Look to suggest visualization improvements. Subscribe to a dashboard for weekly performance updates.

Challenges: Managing permissions to prevent unauthorized access. Ensuring comments are actionable and tracked. Configuring subscriptions for relevant delivery schedules.

Hands-On Example: Scenario: You’re a sales analyst collaborating with your team on a sales dashboard. You’ll share the dashboard and a Look, add comments for feedback, set up a subscription, and manage access, ensuring effective collaboration.

Prerequisites: Computer with Windows 10/11 or macOS 12+ (8 GB RAM, 20 GB free disk space). Looker account. Existing Looks and dashboards from previous weeks.
Step 1: Prepare Content: Use a saved Look and dashboard.
Step 2: Share and Comment: Share content and add comments.
Step 3: Set Subscriptions: Configure email deliveries.
Step 4: Manage Permissions: Handle access settings.
Step 5: Validate and Export: Test and export results.

Interpretation: This hands-on example demonstrates how to use Looker’s collaboration features to share a sales dashboard and Look, provide feedback via comments, and set up subscriptions. By validating sharing and interactivity, you develop practical skills for team collaboration, preparing for the data dictionary in Week 9.

Supplemental Information:

Sharing Content: https://cloud.google.com/looker/docs/sharing-content
Comments in Looker: https://cloud.google.com/looker/docs/comments
Scheduling Deliveries: https://cloud.google.com/looker/docs/scheduling-deliveries

Discussion Points:

How do collaboration features improve team workflows?
Why are permissions critical for secure sharing?
What challenges arise in managing feedback via comments?
How do subscriptions enhance regular reporting?
How can shared folders streamline content access?

Week 9: Introduction to Looker Data Dictionary

Introduction: The Looker Data Dictionary provides a centralized resource for understanding data models, fields, and their definitions, enabling users to explore and use data effectively. This week focuses on how to use Looker’s Data Dictionary to navigate data models, interpret field metadata, and apply this knowledge to query a sales dataset, with a hands-on example emphasizing practical steps to leverage the Data Dictionary for analysis.

Learning Objectives: By the end of this week, you will be able to:

Access and navigate the Looker Data Dictionary.
Understand data models, dimensions, measures, and their metadata (e.g., descriptions, types).
Use the Data Dictionary to select appropriate fields for queries.
Create a Look based on Data Dictionary insights.
Validate query accuracy using Data Dictionary metadata.

Scope: This week covers the Looker Data Dictionary’s structure and usage, building on Week 1’s platform introduction, Week 2’s data exploration, Week 3’s visualizations, Week 4’s dashboards, Week 5’s Looks and UI, Week 6’s filtering and sorting, Week 7’s Look management, and Week 8’s collaboration features. It focuses on practical data model exploration to prepare for report scheduling in Week 10.

Background Information: The Looker Data Dictionary is a metadata hub for data understanding:

Purpose: Documents data models, fields, and their business context. Enables non-technical users to query data confidently.
Key Components: Data Models: Collections of related fields (e.g., “Sales” model). Dimensions: Categorical fields (e.g., Product, Region). Measures: Numeric, aggregated fields (e.g., Sum of Sales). Metadata: Includes field descriptions, data types, sources, and LookML definitions.

Applications: Identify relevant fields for a sales performance query. Understand field calculations (e.g., how Sales is aggregated).

Challenges: Interpreting technical metadata without LookML knowledge. Navigating large data models with many fields. Ensuring field selections align with analysis goals.

Hands-On Example: Scenario: You’re a sales analyst tasked with querying a sales dataset using the Looker Data Dictionary to ensure accurate field selection. You’ll explore the Data Dictionary, select fields for a product sales analysis, create a Look, and validate results.

Prerequisites: Computer with Windows 10/11 or macOS 12+ (8 GB RAM, 20 GB free disk space). Looker account. Sample dataset loaded.
Step 1: Access the Data Dictionary: Log in and navigate to the Data Dictionary.
Step 2: Explore Metadata: Review dimensions, measures, and field details.
Step 3: Create a Look: Use insights to build and save a Look.
Step 4: Validate and Share: Test the Look and share for feedback.
Step 5: Export Results: Export the Look for documentation.

Interpretation: This hands-on example demonstrates how to use Looker’s Data Dictionary to explore a sales dataset’s metadata, select appropriate fields, and create a validated Look. By leveraging the Data Dictionary, you ensure accurate queries, preparing for report scheduling in Week 10.

Supplemental Information:

Looker Data Dictionary: https://cloud.google.com/looker/docs/data-dictionary
LookML Basics: https://cloud.google.com/looker/docs/lookml-basics
Exploring Data Models: https://cloud.google.com/looker/docs/exploring-data

Discussion Points:

How does the Data Dictionary empower non-technical users?
Why is metadata critical for accurate querying?
What challenges arise when navigating large data models?
How can field descriptions improve analysis?
How does the Data Dictionary support collaboration?

Week 10: Basic Report Scheduling & Delivery

Introduction: Report scheduling and delivery in Looker enable users to automate the distribution of Looks and dashboards, ensuring stakeholders receive timely data insights. This week focuses on how to use Looker’s scheduling features to set up automated report deliveries, manage schedules, and validate delivery, with a hands-on example emphasizing practical steps to schedule a sales dashboard and Look.

Learning Objectives: By the end of this week, you will be able to:

Create schedules for Looks and dashboards with specific delivery options (e.g., email, frequency).
Configure schedule settings, including filters and formats (e.g., PNG, CSV).
Manage and edit existing schedules.
Validate report delivery and troubleshoot scheduling issues.
Understand the role of scheduling in data-driven workflows.

Scope: This week covers Looker’s scheduling and delivery features, building on Week 1’s platform introduction, Week 2’s data exploration, Week 3’s visualizations, Week 4’s dashboards, Week 5’s Looks and UI, Week 6’s filtering and sorting, Week 7’s Look management, Week 8’s collaboration, and Week 9’s Data Dictionary. It focuses on practical automation to complete the Looker Foundations course.

Background Information: Looker’s scheduling feature automates report distribution:

Scheduling: Configured for Looks or dashboards via Schedule options. Supports delivery to email, Slack, or other integrations (admin-dependent).
Key Settings: Frequency: One-time, daily, weekly, monthly, or custom intervals. Format: PNG, CSV, PDF, or inline visualization. Filters: Apply specific filters to tailor delivered data. Destinations: Email addresses, groups, or third-party tools.

Applications: Schedule a weekly sales dashboard for executives. Deliver a daily product performance Look to the sales team.

Challenges: Ensuring correct recipient lists and permissions. Managing filter settings to avoid irrelevant data. Troubleshooting delivery failures (e.g., email server issues).

Hands-On Example: Scenario: You’re a sales analyst tasked with automating the delivery of a sales dashboard and a product sales Look to your team. You’ll create schedules, configure delivery options, and validate receipt.

Prerequisites: Computer with Windows 10/11 or macOS 12+ (8 GB RAM, 20 GB free disk space). Looker account. Existing Looks and dashboards from previous weeks.
Step 1: Prepare Content: Use a saved Look and dashboard.
Step 2: Create Schedules: Set up email deliveries.
Step 3: Manage Schedules: Edit and test schedules.
Step 4: Validate Delivery: Check emails and troubleshoot.
Step 5: Export Results: Export scheduled content.

Interpretation: This hands-on example demonstrates how to use Looker’s scheduling features to automate the delivery of a sales dashboard and Look, ensuring timely insights for stakeholders. By managing and validating schedules, you develop practical skills for report automation, completing the Looker Foundations course.

Supplemental Information:

Scheduling Deliveries: https://cloud.google.com/looker/docs/scheduling-deliveries
Managing Schedules: https://cloud.google.com/looker/docs/managing-schedules
Delivery Options: https://cloud.google.com/looker/docs/delivery-options

Discussion Points:

How does scheduling improve reporting efficiency?
Why are filters important in scheduled deliveries?
What challenges arise in managing multiple schedules?
How can delivery formats (e.g., PNG, CSV) serve different stakeholders?
How does automation support data-driven decisions?