Data Engineering with Python
Work with massive datasets to design data models and automate data pipelines using Python
Published on 23. September 2024
356 pages
978-1-83921-230-7 (ISBN)
Description
No detailed description available for "Data Engineering with Python".
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Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
File size
30,27 MB
ISBN-13
978-1-83921-230-7 (9781839212307)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Paul Crickard
Data Engineering with Python
Work with massive datasets to design data models and automate data pipelines using Python
Book
10/2020
Packt Publishing
€57.90
Shipment within 3-4 weeks
Person
Crickard Paul :
Paul Crickard authored a book on the Leaflet JavaScript module. He has been programming for over 15 years and has focused on GIS and geospatial programming for 7 years. He spent 3 years working as a planner at an architecture firm, where he combined GIS with Building Information Modeling (BIM) and CAD. Currently, he is the CIO at the 2nd Judicial District Attorney's Office in New Mexico.
Paul Crickard authored a book on the Leaflet JavaScript module. He has been programming for over 15 years and has focused on GIS and geospatial programming for 7 years. He spent 3 years working as a planner at an architecture firm, where he combined GIS with Building Information Modeling (BIM) and CAD. Currently, he is the CIO at the 2nd Judicial District Attorney's Office in New Mexico.
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
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
