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About Paul Crickard III
Paul Crickard is the author of Data Engineering with Python, Leaflet.js Essentials, and co-author of Mastering Geospatial Analysis with Python.
With a Master's degree in Political Science and a background in Community, and Regional Planning, he combines rigorous social science theory and techniques to technology projects. He has Presented at the New Mexico Big Data and Analytics Summit and the ExperienceIT NM Conference. He has given talks on data to the New Mexico Big Data Working Group, Sandia National Labs, and the New Mexico Geographic Information Council.
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Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects
- 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
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.
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
Explore GIS processing and learn to work with various tools and libraries in Python.
- Analyze and process geospatial data using Python libraries such as; Anaconda, GeoPandas
- Leverage new ArcGIS API to process geospatial data for the cloud.
- Explore various Python geospatial web and machine learning frameworks.
Python comes with a host of open source libraries and tools that help you work on professional geoprocessing tasks without investing in expensive tools. This book will introduce Python developers, both new and experienced, to a variety of new code libraries that have been developed to perform geospatial analysis, statistical analysis, and data management. This book will use examples and code snippets that will help explain how Python 3 differs from Python 2, and how these new code libraries can be used to solve age-old problems in geospatial analysis.
You will begin by understanding what geoprocessing is and explore the tools and libraries that Python 3 offers. You will then learn to use Python code libraries to read and write geospatial data. You will then learn to perform geospatial queries within databases and learn PyQGIS to automate analysis within the QGIS mapping suite. Moving forward, you will explore the newly released ArcGIS API for Python and ArcGIS Online to perform geospatial analysis and create ArcGIS Online web maps. Further, you will deep dive into Python Geospatial web frameworks and learn to create a geospatial REST API.
What you will learn
- Manage code libraries and abstract geospatial analysis techniques using Python 3.
- Explore popular code libraries that perform specific tasks for geospatial analysis.
- Utilize code libraries for data conversion, data management, web maps, and REST API creation.
- Learn techniques related to processing geospatial data in the cloud.
- Leverage features of Python 3 with geospatial databases such as PostGIS, SQL Server, and SpatiaLite.
Who this book is for
The audience for this book includes students, developers, and geospatial professionals who need a reference book that covers GIS data management, analysis, and automation techniques with code libraries built in Python 3.
Table of Contents
- Package installation and management
- Introduction to geospatial code libraries
- Introduction to geospatial databases
- Data types, storage and conversion
- Vector data analysis
- Raster data processing
- Geoprocessing with geodatabases
- Automating QGIS analysis
- ArcGIS API for Python and ArcGIS Online
- Geoprocessing with a GPU Database
- Flask and GeoAlchemy
- Creating a geospatial REST API
- Cloud Geodatabase Analysis and Visualization
- Automating Cloud Cartography
- Python geoprocessing with Hadoop
About This Book
- Leverage the features of existing libraries, plugins, and HTML 5 geolocation APIs to enrich your mapping applications
- Learn the advanced features of Leaflet.js in a streamlined, step-by-step manner
Who This Book Is For
What You Will Learn
- Incorporate Tile Layers and Web Mapping Services into your map
- Write custom functions that use events to make interactive maps
- Add GeoJSON data to a web map
- Create your own images to use as markers on your map
- Build desktop applications using C#
- Make heatmaps and chloropleth maps
- Take advantage of third-party plugins to enhance your map
Starting with how to create maps, you will learn about choosing your basemap and customizing it by adding various attributes such as zoom, start view, pop ups, and so on.
Once you have gained experience of creating your own map with Leaflet, you will move on to combining Leaflet with GeoJSON and adding geometry objects and features to your maps.
Finally, you will understand how Leaflet enables you to use Node.js, C#, and Python alongside its own library to efficiently create maps.