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 data types

When querying data in BigQuery, it is essential to have a solid understanding of the various data types and their characteristics. Data types determine how values are stored, interpreted, and processed in BigQuery. By correctly identifying and handling data types, you can ensure accurate and efficient querying. Let’s explore some key considerations when working with data types in BigQuery.

BigQuery supports a wide range of data types, including integers, floats, strings, Booleans, dates, times, and more. It is more important to use the appropriate data type for each column in your tables to ensure compatibility with the data being stored. We covered the importance of setting the right data types in a table’s setup and schema in Chapter 3, Exploring Data in BigQuery.. When querying data, ensure the data types of the columns being compared or joined are comparable. Incompatible data types may result in unexpected behavior or errors with the query...