Business Intelligence with Looker
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ISBN: 9789365890402
eISBN: 9789365896602
Authors: Shiva Krishna Neeli, Tanya Leung
Rights: Worldwide
Edition: 2025
Pages: 304
Dimension: 7.5*9.25 Inches
Book Type: Paperback
- Description
- Table
- About
Looker is a data analytics tool that is rapidly gaining popularity in the business intelligence space. It has become an essential platform, empowering organizations to analyze data and make informed, data-driven decisions.
In this book, we cover the fundamentals required to jumpstart a Looker instance, starting with dashboard creation and LookML development. You will learn to facilitate every step of the process, from administration to development to visualization creation. This guide will help you understand the Looker platform, from basic user functions to advanced development and administration. It is designed to help you become a Looker expert, enabling you to build a powerful data culture within your organization. Once the foundation is set, we explore advanced LookML concepts, including reusable blocks, Liquid customization, and performance tuning, while also diving into the administrative side of Looker.
By the end of this book, you will possess a complete skill set for leveraging Looker. You will be fully equipped to build and manage sophisticated LookML data models, administer the platform securely, integrate it with other services, and confidently create impactful business intelligence solutions for real-world scenarios.
WHAT YOU WILL LEARN
● Develop LookML code, including core, advanced, and derived table concepts.
● Integrate Looker with mobile apps, Google tools, and external applications.
● Administer Looker, manage security, monitor usage, and tune performance effectively.
● Create, customize, and present diverse reports and dashboards effectively.
● Troubleshoot common issues and implement Looker development best practices.
● Automate Looker tasks and integrate programmatically using APIs/SDKs.
WHO THIS BOOK IS FOR
This book is a valuable resource for new and experienced Looker professionals, including business users, report developers, and LookML developers. It is also ideal for data analysts, data engineers, and business intelligence developers who want to build and administer comprehensive Looker solutions.
1. Getting Started with Looker
2. Creating Reports and Dashboards
3. LookML Development
4. Advanced LookML
5. Beyond Looker
6. Looker Administration
7. Looker Security
8. Troubleshooting, Performance Tuning, and Best Practices
9. Application Programming Interface, Software Development Kit and Embed
10. Looker Project Walkthrough
● Shiva Krishna Neeli is a manager of data engineering at SADA Systems Inc, specializing in business intelligence. He brings extensive experience from his varied roles as a developer, administrator, architect, and manager within business intelligence teams at prominent organizations, including Live Nation and Fidelity Investments. Shiva's academic background includes a master's in computer science from the University of South Carolina, Columbia, SC, earned in 2004. He completed his bachelor's at the Mahatma Gandhi Institute of Technology, Hyderabad, affiliated with Jawaharlal Nehru Technological University, Hyderabad, in 2002.
With a keen interest in business intelligence, data engineering, and artificial intelligence, Shiva has hands-on experience with a wide array of BI tools such as OBIEE, Cognos, Tableau, Domo, and Looker. He has successfully led numerous projects for diverse corporations, from startups to large enterprises. Shiva is also passionate about mentoring developers and analysts across various business intelligence platforms.
● Tanya Leung is a data engineer at SADA Systems Inc, who has worked extensively with several Google Cloud Platform services, most notably Looker. Prior to SADA Systems, Tanya completed her bachelor of science in computer science from the University of Colorado Boulder in 2020.
Tanya has worked extensively on both sides of data, from data processing and pipelining, to data analysis. She seeks to bridge the gap between data engineers and data scientists to maintain data quality from start to finish.