Advanced Analysis and Visualization with Looker
Duration
16-hrs training course with 4 sessions of 4-hrs each
Level
Intermediate to Advanced Level
Design and Tailor this course
As per your team needs
This advanced course on Looker is built for experienced data analysts, data scientists, and data engineers who want to go beyond just fancy reports and visualizations. In this instructor-led advanced course on Analyzing and Visualizing using Looker, you will come across topics such as content management, LookML projects, version control, data groups and caching. These topics are covered with help of expensive hands-on lab exercises to help you gain proficiency.
After completing this course you will be able to:
- Perform Table and Offset Calculations in Looker
- Create and manage Looks, Dashboards, and Folders
- Apply LookMl for creating Dimensions and Measures
- Make use of Model files, Derived tables, Caching, and Data Groups for performance tuning
- Data Engineers responsible for curation and management of data
- Data analysts willing to use LookML to scale their offerings
- Data Storytellers or members of BI teams who have worked on Tableau, Power BI or other similar tools
- Analytical Concepts
- Looker UI
- Pivoting Data in Looker
- Types of table calculations
- Example: Writing and visualizing table calculations
- Types of offset calculations
- Example: Writing offset calculations
- Lab: Working with Table Calculations and Offsets in Looker
- Creating Looks
- Adding filters to a Look
- Delivering data from Looks
- Creating and editing dashboards
- Adding filters to a dashboard
- Delivering data from dashboards
- Organizing content with folders
- Creating and browsing boards
- Understanding your users’ experience
- LookML project hierarchy
- The Looker development environment
- Modeling dimensions
- Example: Creating dimensions using LookML
- Modeling measures
- Example: Creating measures using LookML
- Advanced logic for dimensions and measures
- Lab:: Creating Dimensions and Measures with LookML
- Project version control with Git
- Example: Git workflow in Looker
- How Looker writes SQL
- Explores and join logic
- Symmetric aggregation
- Filtering Explores
- Types of derived tables
- Example: Creating SQL derived tables
- Example: Creating native derived tables
- Useful parameters for native derived tables
- Persistent derived tables
- Lab: Creating Derived Tables with LookML
- Caching in Looker
- Example: Implementing DataGroups in Looker
- Looker Data Explorer
- Filtering and Sorting Data in Looker
- Merging Results from Different Explores in Looker
- Looker Functions and Operators
- Looker Developer
- Creating Measures and Dimensions Using LookML
- Creating Derived Tables Using LookML
- Filtering Explores with LookML
- Caching and Datagroups with LookML
- Getting Started with Liquid to Customize the Looker User Experience
- Enhancing User Interactivity in Looker with Liquid
- Creating dynamic SQL derived tables with LookML and Liquid
- Answering Complex Questions Using Native Derived Tables with LookML
- Modularizing LookML Code with Extends
- Troubleshooting Data Models in Looker
- Employing Best Practices for Improving the Usability of LookML Projects
- Caching and Datagroups with LookML
- Optimizing Performance of LookML Queries
You must have a beginner’s exposure to using Looker, with exposure to Business Intelligence (BI) tools, Data Analytics. A familiarity with Structure Query Language (SQL) and machine learning will be an added advantage.
