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portada Data Exploration and Preparation with BigQuery: A practical guide to cleaning, transforming, and analyzing data for business insights (en Inglés)
Formato
Libro Físico
Idioma
Inglés
N° páginas
264
Encuadernación
Tapa Blanda
Dimensiones
23.5 x 19.1 x 1.4 cm
Peso
0.46 kg.
ISBN13
9781805125266
Categorías

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

Mike Kahn (Autor) · Packt Publishing · Tapa Blanda

Data Exploration and Preparation with BigQuery: A practical guide to cleaning, transforming, and analyzing data for business insights (en Inglés) - Kahn, Mike

Libro Físico

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Reseña del libro "Data Exploration and Preparation with BigQuery: A practical guide to cleaning, transforming, and analyzing data for business insights (en Inglés)"

Leverage BigQuery to understand and prepare your data to ensure that it's accurate, reliable, and ready for analysis and modelingKey FeaturesUse mock datasets to explore data with the BigQuery web UI, bq CLI, and BigQuery API in the Cloud consoleMaster optimization techniques for storage and query performance in BigQueryEngage with case studies on data exploration and preparation for advertising, transportation, and customer support dataPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData 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.What you will learnAssess the quality of a dataset and learn best practices for data cleansingPrepare data for analysis, visualization, and machine learningExplore approaches to data visualization in BigQueryApply acquired knowledge to real-life scenarios and design patternsSet up and organize BigQuery resourcesUse SQL and other tools to navigate datasetsImplement best practices to query BigQuery datasetsGain proficiency in using data preparation tools, techniques, and strategiesWho this book is forThis 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.Table of ContentsIntroducing BigQuery and Its ComponentsBigQuery Organization and DesignExploring Data in BigQueryLoading and Transforming DataQuerying BigQuery DataExploring Data with NotebooksFurther Exploring and Visualizing DataAn Overview of Data Preparation ToolsCleansing and Transforming DataBest Practices for Data Preparation, Optimization, and Cost ControlHands-On Exercise - Analyzing Advertising DataHands-On Exercise Analyzing Transportation DataHands-On Exercise - Analyzing Customer Support DataSummary and Future Directions

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