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

Using SQL for data cleansing and transformation

Data cleansing and data transformation are two main steps in the data preparation process. Data cleansing is the process of identifying and correcting errors in data, while data transformation is the process of converting data from one format or structure into another.

Here are some common examples of data cleansing tasks:

  • Identifying and correcting typos
  • Filling in missing values
  • Formatting data consistently
  • Removing duplicate records

Here are some examples of data transformation tasks:

  • Converting data from one format to another
  • Aggregating data (for example, summing sales figures by month)
  • Normalizing data (for example, converting all dates into the same format)
  • Formatting data for visualizations or machine learning

Now that you understand some scenarios where data cleansing and transformation would be useful, let’s look into some examples using SQL so that you understand...