Creating Business Value with Data and Looker

In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.

This is a Google Cloud Self-Paced Lab. In this lab, you will learn how to utilize custom fields in Looker Explores queries. Looker provides the ability for non-developer users to create and utilize ad hoc fields for richer data analysis. This is done by creating custom measures, custom dimensions, table calculations and using custom groupings to narrow down data to match specific conditions. Using ad hoc fields gives non-developers the ability to create new fields, as opposed to regular fields, which require that you have development permissions and understand LookML allowing them to have more flexibility in finding data they are looking for independently.

This is a self-paced lab that takes place in the Google Cloud console. Looker provides the ability for LookML developers to build modeled queries that help all Looker business users quickly get started and easily explore data in the platform. This three-task lab will provide you with practical, hands-on experience using the query parameter to create a modeled query for an Explore. The modeled query is listed in the Quick Start section of a blank Explore. Modeled queries are extremely helpful to Looker business users, since you can specify elements like the dimensions, measures, filters, and pivots that may be the most relevant and insightful. In this lab you will learn how to create a modeled query, which can be a helpful starting point for users who are new to Looker and learning how to query data.

This is a Google Cloud Self-Paced Lab. In this lab, you'll learn the best methods to optimize query performance in Looker. Looker is a modern data platform in Google Cloud that you can use to analyze and visualize your data interactively. You can use Looker to do in-depth data analysis, integrate insights across different data sources, build actionable data-driven workflows, and create custom data applications. Big, complex queries can be costly, and running them repeatedly strains your database, thereby reducing performance. Ideally, you want to avoid re-running massive queries if nothing has changed, and instead, append new data to existing results to reduce repetitive requests. Although there are many ways to optimize performance of LookML queries, this lab focuses on the most commonly used methods to optimize query performance in Looker: persistent derived tables, aggregate awareness, and performantly joining views.

In this introductory course, you'll learn how Looker can help you explore, analyze, and visualize your data to drive better decisions. Through a combination of video lectures and demos, you'll discover how to connect to various data sources, build interactive dashboards, and perform effective data analysis. Whether you're a data analyst, BI analyst, data scientist or business user, this course will equip you with the foundational knowledge to start using Looker effectively, regardless of your background.