Data engineering python work de crickard paul (15 resultados)
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Librería: World of Books (was SecondSale), Montgomery, IL, Estados Unidos de AmericaWorld of Books (was SecondSale)
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Paperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.
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Librería: World of Books Inc, Montgomery, IL, Estados Unidos de AmericaWorld of Books Inc
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Paperback. Condición: Good. Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and l…earn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book 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 learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is forThis 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.
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Librería: Goodbooks Company, Springdale, AR, Estados Unidos de AmericaGoodbooks Company
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EUR 33,45
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Condición: good. Book has corner edge dings and or scratches and signs of wear.
Idioma: Inglés
Editorial: Packt Publishing Limited, United Kingdom, Birmingham, 2020
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Librería: WorldofBooks, Goring-By-Sea, WS, Reino UnidoWorldofBooks
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EUR 35,26
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Paperback. Condición: Very Good. Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models…and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book 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. Youll 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. Youll 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, youll build architectures on which youll learn how to deploy data pipelines. By the end of this Python book, youll 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 learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is forThis 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. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 57,19
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Condición: New. pp. 356.
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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Librería: GoldBooks, Denver, CO, Estados Unidos de AmericaGoldBooks
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EUR 74,84
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Paperback. Condición: new. New Copy. Customer Service Guaranteed.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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Condición: New. Print on Demand pp. 356.
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 54,84
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Condición: New. PRINT ON DEMAND pp. 356.
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Librería: THE SAINT BOOKSTORE, Southport, Reino UnidoTHE SAINT BOOKSTORE
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EUR 60,80
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Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Librería: preigu, Osnabrück, Alemaniapreigu
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EUR 65,50
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Taschenbuch. Condición: Neu. Data Engineering with Python | Work with massive datasets to design data models and automate data pipelines using Python | Paul Crickard | Taschenbuch | Kartoniert / Broschiert | Englisch | 2020 | Packt Publishing | EAN 9781839214189 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 362…44 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 75,44
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Taschenbuch. 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 ski…lls 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.
