Book Image

Data Engineering with Python

By : Paul Crickard
Book Image

Data Engineering with Python

By: Paul Crickard

Overview of this book

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.
Table of Contents (21 chapters)
Section 1: Building Data Pipelines – Extract Transform, and Load
Section 2:Deploying Data Pipelines in Production
Section 3:Beyond Batch – Building Real-Time Data Pipelines

Data engineering versus data science

Data engineering is what makes data science possible. Again, depending on the maturity of an organization, data scientists may be expected to clean and move the data required for analysis. This is not the best use of a data scientist's time. Data scientists and data engineers use similar tools (Python, for instance), but they specialize in different areas. Data engineers need to understand data formats, models, and structures to efficiently transport data, whereas data scientists utilize them for building statistical models and mathematical computation.

Data scientists will connect to the data warehouses built by data engineers. From there, they can extract the data required for machine learning models and analysis. Data scientists may have their models incorporated into a data engineering pipeline. A close relationship should exist between data engineers and data scientists. Understanding what data scientists need in the data will only...