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Data Exploration and Preparation with BigQuery
Data Exploration and Preparation with BigQuery

Data Exploration and Preparation with BigQuery: A practical guide to cleaning, transforming, and analyzing data for business insights

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Data Exploration and Preparation with BigQuery

Introducing BigQuery and Its Components

BigQuery is a fully managed serverless data warehouse that helps users and enterprises manage and analyze data with SQL. It includes features for data sharing, machine learning, geospatial analysis, and business intelligence.

In this chapter, you will be introduced to BigQuery and how the service operates. By gaining an understanding of how this service is delivered and how it functions, you will be able to use BigQuery more effectively.

This chapter will introduce and describe the first two, most critical steps of data analysis: data exploration and data preparation. By the end of this chapter, you will have a solid foundational understanding of BigQuery and how to begin preparing data to make it more useful and insightful.

In this chapter, we’re going to cover the following main topics:

  • What is BigQuery?
  • How does BigQuery work?
  • BigQuery administration and access
  • BigQuery best practices and cost management...

Technical requirements

To get the most out of this chapter and the book, you will want to set up access to the Google Cloud console (https://console.cloud.google.com/). At the time of writing this publication, there is a free trial that will allow you to explore and build resources. Many services are also eligible for the Free tier (https://cloud.google.com/free), including BigQuery, which includes 1 TB of querying per month and 10 GB of storage each month. All you will need to get started is a Google account (most people know this as their Gmail account). If you wish to use the Cloud console with your work email, you may be prompted to contact your organization administrator.

If you wish to explore BigQuery at no cost to determine whether BigQuery fits your needs, you can also utilize the BigQuery sandbox (https://cloud.google.com/bigquery/docs/sandbox). The sandbox lets you experience BigQuery and the Google Cloud console without enabling billing for your project. To access the...

What is BigQuery?

BigQuery is Google Cloud’s enterprise data warehouse, designed to help users ingest, store, visualize, and analyze data. BigQuery enables users to unlock insights across petabyte-sized datasets and enables business agility. In this chapter, we will explore BigQuery to give you a complete understanding of how the service operates so you can best utilize BigQuery to prepare and explore your data.

BigQuery’s managed serverless infrastructure unlocks users from managing resources and allows for greater focus on data. BigQuery is a serverless enterprise data warehouse with analytic tools and machine learning services, with multi-cloud capabilities. BigQuery stores data in a columnar format that is optimized for analytics queries on structured and semi-structured data.

There are several ways to get data into BigQuery. You can ingest data into BigQuery by uploading it in one-time or scheduled batch loads or by streaming data to enable real-time insights...

Understanding how BigQuery works

BigQuery is known for being a serverless, highly scalable, cost-effective cloud data warehouse. The power of this cloud-native service lies in the decoupled storage and compute resources. Unlike other data warehouse software or services, BigQuery service architecture has independent storage and compute infrastructure layers. This allows each layer to scale independently on demand. This decoupled architecture offers high flexibility and cost control for data analytics and data science workloads.

Underneath the user interface, BigQuery is powered by several Google technologies that have been in use since well before the 2011 general availability launch of this service. In this section, we will go over the primary technologies behind BigQuery – Dremel, Colossus, Borg, and Jupiter, so you can better understand how BigQuery is different from other enterprise data warehouse services.

Dremel, the execution engine

Dremel is the service that...

BigQuery administration and access

In this section, we will discuss how to administer and secure resources in BigQuery. We will describe access methods for users and services to access data. By the end of the section, you will have a basic understanding of the different approaches to securely allocate resources in BigQuery and how to enable access to users.

Tools for administration

BigQuery has several access methods for administration tasks. Most users and roles can do the same function, with multiple tools enabling point-and-click as well as automated task flows.

The Google Cloud console is the main user interface for the centralized management of data and compute resources. Users access the Google Cloud console to run SQL queries and create and manage BigQuery resources. In the BigQuery navigation menu, you can access options for analysis, migration, and administration.

When you access the SQL workspace in the BigQuery section of the Cloud console, you will be presented...

BigQuery best practices and cost management

In this section, we will go over general best practices for using BigQuery. We will also touch on cost management and the different approaches to optimizing your spending on BigQuery. By the end of this section, you will have a good understanding of how to efficiently use BigQuery to control costs and optimize storage and queries.

Best practices

There are many best practices for using BigQuery. The following are a few best practices that are foundational for using this service efficiently. Take note and implement these approaches to get the most performance and benefit out of BigQuery:

  • Use the right data type for your data: BigQuery supports many data types, each with its own storage cost. Keep in mind that a large string column takes up more space than an integer column. When designing your table schema, make sure to choose the right data type for your data.
  • Query only the columns you need: Avoid using SELECT * as this...

Extending your data

In the world of data analytics and decision-making, having access to comprehensive and enriched datasets is crucial. The ability to extend data by incorporating external sources, using machine learning, and augmenting it with additional context can unlock new insights and empower organizations to make more informed decisions. In this chapter, we are exploring the techniques and strategies for extending data in BigQuery. This section equips data analysts and engineers with the tools and knowledge to extend their datasets and extract deeper value from their data.

BigQuery ML

BigQuery ML or BQML is a machine learning service that can be used to build and deploy machine learning models in BigQuery using SQL queries. BigQuery ML allows data analysts with limited programming expertise the ability to build models using their existing SQL expertise. This capability in BigQuery allows teams and organizations with limited data science resources the ability to use machine...

Summary

In this chapter, we described BigQuery and gained an understanding of the Google services that deliver the petabyte-scale trillion-row analytic serverless data warehouse. We discussed tools for the administration of BigQuery resources and went over IAM, including how to secure access, down to the table level.

We discussed cost and how to control costs via pricing models and query and table best practices. In the latter part of this chapter, we described how to extend data in BigQuery with BigQuery ML (BQML), public datasets, and external connections. By learning how BigQuery works, data analysts will be ready to gain skills to use this powerful data warehouse to reduce time to insights and derive more business value from large-scale datasets.

In the next chapter, we will go further into BigQuery organization and design. You will master BigQuery resource hierarchy and schema design practices.

References

The following are the links to the sources and documentation that we referred to in this chapter:

  1. A look inside Google’s Data Center Networks: https://cloudplatform.googleblog.com/2015/06/A-Look-Inside-Googles-Data-Center-Networks.html
  2. Roles and permissions: https://cloud.google.com/iam/docs/roles-overview
  3. Estimate and control costs: https://cloud.google.com/bigquery/docs/best-practices-costs
  4. Query partitioned tables: https://cloud.google.com/bigquery/docs/querying-partitioned-tables
  5. Dremel: Interactive Analysis of Web-Scale Datasets: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36632.pdf or https://research.google/pubs/pub36632/
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Key benefits

  • Use mock datasets to explore data with the BigQuery web UI, bq CLI, and BigQuery API in the Cloud console
  • Master optimization techniques for storage and query performance in BigQuery
  • Engage with case studies on data exploration and preparation for advertising, transportation, and customer support data
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

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.

Who is this book for?

This book is for data analysts seeking to enhance their data exploration and preparation skills using BigQuery. It guides anyone using BigQuery as a data warehouse to extract business insights from large datasets. A basic understanding of SQL, reporting, data modeling, and transformations will assist with understanding the topics covered in this book.

What you will learn

  • Assess the quality of a dataset and learn best practices for data cleansing
  • Prepare data for analysis, visualization, and machine learning
  • Explore approaches to data visualization in BigQuery
  • Apply acquired knowledge to real-life scenarios and design patterns
  • Set up and organize BigQuery resources
  • Use SQL and other tools to navigate datasets
  • Implement best practices to query BigQuery datasets
  • Gain proficiency in using data preparation tools, techniques, and strategies
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Length: 264 pages
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Language : English
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Table of Contents

20 Chapters
Part 1: Introduction to BigQuery Chevron down icon Chevron up icon
Chapter 1: Introducing BigQuery and Its Components Chevron down icon Chevron up icon
Chapter 2: BigQuery Organization and Design Chevron down icon Chevron up icon
Part 2: Data Exploration with BigQuery Chevron down icon Chevron up icon
Chapter 3: Exploring Data in BigQuery Chevron down icon Chevron up icon
Chapter 4: Loading and Transforming Data Chevron down icon Chevron up icon
Chapter 5: Querying BigQuery Data Chevron down icon Chevron up icon
Chapter 6: Exploring Data with Notebooks Chevron down icon Chevron up icon
Chapter 7: Further Exploring and Visualizing Data Chevron down icon Chevron up icon
Part 3: Data Preparation with BigQuery Chevron down icon Chevron up icon
Chapter 8: An Overview of Data Preparation Tools Chevron down icon Chevron up icon
Chapter 9: Cleansing and Transforming Data Chevron down icon Chevron up icon
Chapter 10: Best Practices for Data Preparation, Optimization, and Cost Control Chevron down icon Chevron up icon
Part 4: Hands-On and Conclusion Chevron down icon Chevron up icon
Chapter 11: Hands-On Exercise – Analyzing Advertising Data Chevron down icon Chevron up icon
Chapter 12: Hands-On Exercise – Analyzing Transportation Data Chevron down icon Chevron up icon
Chapter 13: Hands-On Exercise – Analyzing Customer Support Data Chevron down icon Chevron up icon
Chapter 14: Summary and Future Directions Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Amazon Customer Dec 09, 2023
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The author is successfully able to present the material in a clear and concise way. I found the book extremely helpful! Highly recommend!
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Kindle Customer Dec 09, 2023
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In today’s world where data is of utmost importance, this book provides a wealth of knowledge into various insights for business to leverage to apply real world strategies. Highly recommend as an easy to follow and informative guide for anyone looking to learn more about making better data supported decisions.
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Thomas Frantzen Dec 14, 2023
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An essential guide for anyone in data and mastering BigQuery! It goes beyond the basics with comprehensive insights, practical techniques, and realistic examples for data exploration, preparation, and analysis. Super helpful!
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Amazon Customer Dec 09, 2023
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I found the information very helpful. Well done......
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Ross K. Dec 09, 2023
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Book is a wealth of knowledge and deep dive into big query and data exploration. Highly recommend.
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