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Fundamentals Analytics Engineering Introduction by Dumky Wilde (11 results)
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Fundamentals of Analytics Engineering: An introduction to building end-to-end analytics solutions (Paperback or Softback)
Published by Packt Publishing 3/29/2024, 2024
ISBN 10: 1837636451 ISBN 13: 9781837636457
Language: English
Paperback or Softback. Condition: New. Fundamentals of Analytics Engineering: An introduction to building end-to-end analytics solutions 1.26. Book.
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Fundamentals of Analytics Engineering: An introduction to building end-to-end analytics solutions
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Fundamentals of Analytics Engineering: An introduction to building end-to-end analytics solutions
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Add to basketCondition: Gut. Zustand: Gut - Gebrauchs- und Lagerspuren. Außen: angestoßen. Innen: Geknickt. | Seiten: 332 | Sprache: Englisch | Produktart: Bücher.
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Fundamentals of Analytics Engineering: An introduction to building end-to-end analytics solutions
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Fundamentals of Analytics Engineering: An introduction to building end-to-end analytics solutions
Published by Packt Publishing Limited, 2024
ISBN 10: 1837636451 ISBN 13: 9781837636457
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineeringKey Features Discover how analytics engineering aligns with your organization's data strategy Access insights shared by a team of seven industry experts Tackle common analytics engineering problems faced by modern businesses Purchase of the print or Kindle book includes a free PDF Elektronisches BuchBook DescriptionWritten by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer.After conquering data ingestion and techniques for data quality and scalability, you'll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You'll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You'll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance.By the end of this book, you'll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.What you will learn Design and implement data pipelines from ingestion to serving data Explore best practices for data modeling and schema design Scale data processing with cloud based analytics platforms and tools Understand the principles of data quality management and data governance Streamline code base with best practices like collaborative coding, version control, reviews and standards Automate and orchestrate data pipelines Drive business adoption with effective scoping and prioritization of analytics use casesWho this book is forThis book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing.Table of Contents What is Analytics Engineering The Modern Data Stack Data Ingestion Data Warehouses Data Modeling Data Transformation Serving Data Hands-on: Building a Data Platform Data Quality & Observability Writing Code in a Team Writing Robust Pipelines Gathering Business Requirements Documenting Business Logic Data Governance.
