Data Engineering with Python: work with massive datasets to design data models and automate data pipelines using python
Data 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 challen...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Körperschaft: | |
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing
2020
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| Ausgabe: | 1st edition |
| Schlagworte: | |
| Zusammenfassung: | Data 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. |
| Beschreibung: | XII, 337 Seiten |
| ISBN: | 9781839214189 |
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| 505 | 8 | |a 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.What is Data Engineering? Building Our Data Engineering Infrastructure Reading and Writing Files Working with Databases Cleaning, Transforming, and Enriching Data Building a 311 Data Pipeline Features of a Production Pipeline Version Control Using the NiFi Registry Monitoring and Logging Pipelines Deploying your Pipelines Building a Production Data Pipeline Building a Kafka Cluster Streaming Data with Apache Kafka Data Processing with Apache Spark Real-Time Edge Data with MiNiFi, Kafka, and Spark Appendix | |
| 520 | 3 | |a Data 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. | |
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| contents | 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.What is Data Engineering? Building Our Data Engineering Infrastructure Reading and Writing Files Working with Databases Cleaning, Transforming, and Enriching Data Building a 311 Data Pipeline Features of a Production Pipeline Version Control Using the NiFi Registry Monitoring and Logging Pipelines Deploying your Pipelines Building a Production Data Pipeline Building a Kafka Cluster Streaming Data with Apache Kafka Data Processing with Apache Spark Real-Time Edge Data with MiNiFi, Kafka, and Spark Appendix |
| ctrlnum | (OCoLC)1284795409 (DE-599)BVBBV047551575 |
| discipline | Informatik Mathematik Geographie |
| discipline_str_mv | Informatik Mathematik Geographie |
| edition | 1st edition |
| format | Book |
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| index_date | 2024-07-03T18:24:44Z |
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| isbn | 9781839214189 |
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| spelling | Crickard, Paul Verfasser (DE-588)108210499X aut Data Engineering with Python work with massive datasets to design data models and automate data pipelines using python Crickard, Paul 1st edition Birmingham Packt Publishing 2020 XII, 337 Seiten txt rdacontent n rdamedia nc rdacarrier 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.What is Data Engineering? Building Our Data Engineering Infrastructure Reading and Writing Files Working with Databases Cleaning, Transforming, and Enriching Data Building a 311 Data Pipeline Features of a Production Pipeline Version Control Using the NiFi Registry Monitoring and Logging Pipelines Deploying your Pipelines Building a Production Data Pipeline Building a Kafka Cluster Streaming Data with Apache Kafka Data Processing with Apache Spark Real-Time Edge Data with MiNiFi, Kafka, and Spark Appendix Data 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. Angewandte Mathematik (DE-588)4142443-8 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Datenauswertung (DE-588)4131193-0 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Programmiersprache (DE-588)4047409-4 gnd rswk-swf Geowissenschaften (DE-588)4020288-4 gnd rswk-swf Berechnung (DE-588)4120997-7 gnd rswk-swf Python 3.x (DE-588)7692360-5 gnd rswk-swf Geostatistik (DE-588)4020279-3 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Umweltwissenschaften (DE-588)4137364-9 gnd rswk-swf Python 3.x (DE-588)7692360-5 s Programmiersprache (DE-588)4047409-4 s Geowissenschaften (DE-588)4020288-4 s Umweltwissenschaften (DE-588)4137364-9 s Geostatistik (DE-588)4020279-3 s Datenauswertung (DE-588)4131193-0 s DE-604 Angewandte Mathematik (DE-588)4142443-8 s Statistik (DE-588)4056995-0 s Datenanalyse (DE-588)4123037-1 s Berechnung (DE-588)4120997-7 s Python Programmiersprache (DE-588)4434275-5 s Maschinelles Lernen (DE-588)4193754-5 s Safari, an O’Reilly Media Company ctb |
| spellingShingle | Crickard, Paul Data Engineering with Python work with massive datasets to design data models and automate data pipelines using python 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.What is Data Engineering? Building Our Data Engineering Infrastructure Reading and Writing Files Working with Databases Cleaning, Transforming, and Enriching Data Building a 311 Data Pipeline Features of a Production Pipeline Version Control Using the NiFi Registry Monitoring and Logging Pipelines Deploying your Pipelines Building a Production Data Pipeline Building a Kafka Cluster Streaming Data with Apache Kafka Data Processing with Apache Spark Real-Time Edge Data with MiNiFi, Kafka, and Spark Appendix Angewandte Mathematik (DE-588)4142443-8 gnd Statistik (DE-588)4056995-0 gnd Datenauswertung (DE-588)4131193-0 gnd Datenanalyse (DE-588)4123037-1 gnd Programmiersprache (DE-588)4047409-4 gnd Geowissenschaften (DE-588)4020288-4 gnd Berechnung (DE-588)4120997-7 gnd Python 3.x (DE-588)7692360-5 gnd Geostatistik (DE-588)4020279-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd Umweltwissenschaften (DE-588)4137364-9 gnd |
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| title | Data Engineering with Python work with massive datasets to design data models and automate data pipelines using python |
| title_auth | Data Engineering with Python work with massive datasets to design data models and automate data pipelines using python |
| title_exact_search | Data Engineering with Python work with massive datasets to design data models and automate data pipelines using python |
| title_exact_search_txtP | Data Engineering with Python work with massive datasets to design data models and automate data pipelines using python |
| title_full | Data Engineering with Python work with massive datasets to design data models and automate data pipelines using python Crickard, Paul |
| title_fullStr | Data Engineering with Python work with massive datasets to design data models and automate data pipelines using python Crickard, Paul |
| title_full_unstemmed | Data Engineering with Python work with massive datasets to design data models and automate data pipelines using python Crickard, Paul |
| title_short | Data Engineering with Python |
| title_sort | data engineering with python work with massive datasets to design data models and automate data pipelines using python |
| title_sub | work with massive datasets to design data models and automate data pipelines using python |
| topic | Angewandte Mathematik (DE-588)4142443-8 gnd Statistik (DE-588)4056995-0 gnd Datenauswertung (DE-588)4131193-0 gnd Datenanalyse (DE-588)4123037-1 gnd Programmiersprache (DE-588)4047409-4 gnd Geowissenschaften (DE-588)4020288-4 gnd Berechnung (DE-588)4120997-7 gnd Python 3.x (DE-588)7692360-5 gnd Geostatistik (DE-588)4020279-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd Umweltwissenschaften (DE-588)4137364-9 gnd |
| topic_facet | Angewandte Mathematik Statistik Datenauswertung Datenanalyse Programmiersprache Geowissenschaften Berechnung Python 3.x Geostatistik Maschinelles Lernen Python Programmiersprache Umweltwissenschaften |
| work_keys_str_mv | AT crickardpaul dataengineeringwithpythonworkwithmassivedatasetstodesigndatamodelsandautomatedatapipelinesusingpython AT safarianoreillymediacompany dataengineeringwithpythonworkwithmassivedatasetstodesigndatamodelsandautomatedatapipelinesusingpython |