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Data Exploration and Preparation with BigQuery

Data Exploration and Preparation with BigQuery

By : Mike Kahn
5 (17)
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Data Exploration and Preparation with BigQuery

Data Exploration and Preparation with BigQuery

5 (17)
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)
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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

Exploring architecture patterns

This section presents three architecture patterns for organizing BigQuery resources. The following three patterns should cover most of the common usage patterns for teams and organizations using BigQuery. It is important to understand these architecture patterns to determine what setup in BigQuery will work best for your usage. Each pattern has its advantages and most organizations combine elements of multiple patterns. We will lead with the recommended design and outline alternative approaches, including the considerations for each one:

  • Centralized enterprise data warehouse: The BI and data team owns all department or business unit data. Departments have their own projects with views and dashboards for utilizing the organization’s enterprise data warehouse.
  • Decentralized enterprise data warehouse: Each department owns its raw data. The organization creates a central data warehouse project for analysis.
  • Cross-org data exchange...

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