Burlington Public Library MA

Fundamentals of data engineering, plan and build robust data systems, Joe Reis and Matt Housley

Label
Fundamentals of data engineering, plan and build robust data systems, Joe Reis and Matt Housley
Language
eng
Bibliography note
Includes bibliographical references and index
Illustrations
illustrations
Index
index present
Literary Form
non fiction
Main title
Fundamentals of data engineering
Nature of contents
bibliography
Oclc number
1334138491
Responsibility statement
Joe Reis and Matt Housley
Sub title
plan and build robust data systems
Summary
Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you will learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available in the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You will understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, governance, and deployment that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape ; Assess data engineering problems using an end-to-end data framework of best practices ; Cut through marketing hype when choosing data technologies, architecture, and processes ; Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle
Table Of Contents
Data engineering described -- The data engineering lifecycle -- Designing good data architecture -- Choosing technologies across the data engineering lifecycle -- Data generation in source systems -- Storage -- Ingestion -- Queries, modeling, and transformation -- Serving data for analytics, machine learning, and reverse ETL -- Security and privacy -- The future of data engineering
Classification
Mapped to

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