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Librería: World of Books (was SecondSale), Montgomery, IL, Estados Unidos de America
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Librería: medimops, Berlin, Alemania
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Idioma: Inglés
Publicado por Packt Publishing, Limited, 2020
ISBN 10: 183921418X ISBN 13: 9781839214189
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Librería: GoldBooks, Denver, CO, Estados Unidos de America
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Idioma: Inglés
Publicado por Packt Publishing, Limited, 2020
ISBN 10: 183921418X ISBN 13: 9781839214189
Librería: Majestic Books, Hounslow, Reino Unido
EUR 52,89
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Añadir al carritoCondición: New. Print on Demand pp. 356.
Idioma: Inglés
Publicado por Packt Publishing, Limited, 2020
ISBN 10: 183921418X ISBN 13: 9781839214189
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 53,97
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Añadir al carritoCondición: New. PRINT ON DEMAND pp. 356.
Idioma: Inglés
Publicado por Packt Publishing Limited, 2020
ISBN 10: 183921418X ISBN 13: 9781839214189
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EUR 60,15
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Librería: preigu, Osnabrück, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Data Engineering with Python | Work with massive datasets to design data models and automate data pipelines using Python | Paul Crickard | Taschenbuch | Englisch | 2020 | Packt Publishing | EAN 9781839214189 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 73,63
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projectsKey features:Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examplesDesign data models and learn how to extract, transform, and load (ETL) data using PythonSchedule, automate, and monitor complex data pipelines in productionBook DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python.The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You'll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You'll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you'll build architectures on which you'll learn how to deploy data pipelines.By the end of this Python book, you'll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learnUnderstand how data engineering supports data science workflowsDiscover how to extract data from files and databases and then clean, transform, and enrich itConfigure processors for handling different file formats as well as both relational and NoSQL databasesFind out how to implement a data pipeline and dashboard to visualize resultsUse staging and validation to check data before landing in the warehouseBuild real-time pipelines with staging areas that perform validation and handle failuresGet to grips with deploying pipelines in the production environmentWho this book is for¿This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required.