Book Image

Data Exploration and Preparation with BigQuery

By : Mike Kahn
Book Image

Data Exploration and Preparation with BigQuery

By: Mike Kahn

Overview of this book

Data professionals encounter a multitude of challenges such as handling large volumes of data, dealing with data silos, and the lack of appropriate tools. Datasets often arrive in different conditions and formats, demanding considerable time from analysts, engineers, and scientists to process and uncover insights. The complexity of the data life cycle often hinders teams and organizations from extracting the desired value from their data assets. Data Exploration and Preparation with BigQuery offers a holistic solution to these challenges. The book begins with the basics of BigQuery while covering the fundamentals of data exploration and preparation. It then progresses to demonstrate how to use BigQuery for these tasks and explores the array of big data tools at your disposal within the Google Cloud ecosystem. The book doesn’t merely offer theoretical insights; it’s a hands-on companion that walks you through properly structuring your tables for query efficiency and ensures adherence to data preparation best practices. You’ll also learn when to use Dataflow, BigQuery, and Dataprep for ETL and ELT workflows. The book will skillfully guide you through various case studies, demonstrating how BigQuery can be used to solve real-world data problems. By the end of this book, you’ll have mastered the use of SQL to explore and prepare datasets in BigQuery, unlocking deeper insights from data.
Table of Contents (21 chapters)
Free Chapter
1
Part 1: Introduction to BigQuery
4
Part 2: Data Exploration with BigQuery
10
Part 3: Data Preparation with BigQuery
14
Part 4: Hands-On and Conclusion

Understanding how BigQuery works

BigQuery is known for being a serverless, highly scalable, cost-effective cloud data warehouse. The power of this cloud-native service lies in the decoupled storage and compute resources. Unlike other data warehouse software or services, BigQuery service architecture has independent storage and compute infrastructure layers. This allows each layer to scale independently on demand. This decoupled architecture offers high flexibility and cost control for data analytics and data science workloads.

Underneath the user interface, BigQuery is powered by several Google technologies that have been in use since well before the 2011 general availability launch of this service. In this section, we will go over the primary technologies behind BigQuery – Dremel, Colossus, Borg, and Jupiter, so you can better understand how BigQuery is different from other enterprise data warehouse services.

Dremel, the execution engine

Dremel is the service that...