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

Summary of key points

Let’s revise our chapter-wise journey in this book right from the beginning in the following sections, which contain a list of key points from each chapter.

Chapter 1, Introducing BigQuery and Its Components

  • BigQuery is a fully managed, serverless data warehouse that enables users to analyze data with SQL.
  • BigQuery uses a columnar format that is optimized for analytics queries on structured and semi-structured data.
  • You can use common SQL queries to analyze data in BigQuery, as BigQuery supports a standard SQL dialect known as GoogleSQL.
  • There are several access methods for administration tasks in BigQuery, including the Google Cloud console, the bq command-line tool, and the BigQuery API.
  • Role-based access control (RBAC) is used to secure data and resources. You can control access to rows as well as views.
  • There are two pricing models: on-demand analysis and capacity pricing (BigQuery Editions), offering predictable pricing...