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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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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

Cleansing and Transforming Data

Data engineers and analysts can spend massive amounts of time transforming, cleaning, and preparing data. They know it is not possible to generate accurate reporting and models with corrupted or incomplete data. With the variability of systems reports, you will likely encounter datasets that need to be manipulated in your future as a data analyst. In this chapter, we will dive deeper into data preparation. We will focus on cleansing and transforming data and provide you with approaches, strategies, and repeatable code and guidance that will help you improve the quality of your data in BigQuery.

Cleansing and transforming data can be done at various times in the data life cycle. Also known as data pre-processing, the goal of cleansing and transforming data is to enhance the performance of your data. We touched on transforming data alongside the loading process in Chapter 4, Loading and Transforming Data. Now, we will continue and discuss cleansing...

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