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Data Engineering with Python : Work with Massive Datasets to Design Data Models
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Libro en perfecto estado y poco leído. La tapa no tiene desperfectos y si procede, con sobrecubierta para las tapas duras. Incluye todas las páginas sin arrugas ni roturas. El texto no está subrayado ni resaltado de forma alguna, y no hay anotaciones en los márgenes. Puede presentar marcas de identificación mínimas en la contraportada o las guardas. Muy poco usado. Consulta el anuncio del vendedor para obtener más información y la descripción de cualquier posible imperfección.
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N.º de artículo de eBay:297096014072
Características del artículo
- Estado
- ISBN
- 9781839214189
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Product Identifiers
Publisher
Packt Publishing, The Limited
ISBN-10
183921418X
ISBN-13
9781839214189
eBay Product ID (ePID)
3050401243
Product Key Features
Number of Pages
356 Pages
Publication Name
Data Engineering with Python : Work with Massive Datasets to Design Data Models and Automate Data Pipelines Using Python
Language
English
Subject
Data Modeling & Design, Databases / Data Warehousing, Data Processing
Publication Year
2020
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Length
3.6 in
Item Width
3 in
Additional Product Features
Intended Audience
Trade
Table Of Content
Table of Contents 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
Synopsis
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 Description 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. 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 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., 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 Description 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. 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 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., This book is a comprehensive introduction to building data pipelines, that will have you moving and transforming data in no time. You'll learn how to build data pipelines, transform and clean data, and deliver it to provide value to users. You will learn to deploy production data pipelines that include logging, monitoring, and version control.
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