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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. 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.
Explore GIS processing and learn to work with various tools and
libraries in Python. Key Features 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. Book
DescriptionPython 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 forThe 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.
If you are a web developer working with geospatial concepts and
mapping APIs, and you want to learn Leaflet to create mapping
solutions, this book is for you. You need to have a basic knowledge
of working with JavaScript and performing web application
development.
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