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

Loading CSV data files from local upload

In this example scenario, we will load three advertising and sales data sources into a dataset in BigQuery for analysis. Each data source CSV file will be loaded manually through a single batch job. Loading CSV files has the advantage of being an easy process, as they can be loaded from local upload or Google Cloud Storage.

This approach has disadvantages as well. First, the data will only be from a snapshot or a moment in time, and second, it is a manual operation – it needs to be done by an individual. Beyond this example, with moment-in-time data, you may consider setting up streaming ingestion when possible for automated data loading. Many common advertising or marketing services support integration with BigQuery to handle data loading and updates for you. For additional information on loading data into BigQuery, review Chapter 4, Loading and Transforming Data.

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