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Data Engineering with Python

By Paul Crickard
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  1. Free Chapter
    Chapter 1: What is Data Engineering?
About 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.
Publication date:
October 2020
Publisher
Packt
Pages
356
ISBN
9781839214189

 

Chapter 1: What is Data Engineering?

Welcome to Data Engineering with Python. While data engineering is not a new field, it seems to have stepped out from the background recently and started to take center stage. This book will introduce you to the field of data engineering. You will learn about the tools and techniques employed by data engineers and you will learn how to combine them to build data pipelines. After completing this book, you will be able to connect to multiple data sources, extract the data, transform it, and load it into new locations. You will be able to build your own data engineering infrastructure, including clustering applications to increase their capacity to process data.

In this chapter, you will learn about the roles and responsibilities of data engineers and how data engineering works to support data science. You will be introduced to the tools used by data engineers, as well as the different areas of technology that you will need to be proficient in to...

 

What data engineers do

Data engineering is part of the big data ecosystem and is closely linked to data science. Data engineers work in the background and do not get the same level of attention as data scientists, but they are critical to the process of data science. The roles and responsibilities of a data engineer vary depending on an organization's level of data maturity and staffing levels; however, there are some tasks, such as the extracting, loading, and transforming of data, that are foundational to the role of a data engineer.

At the lowest level, data engineering involves the movement of data from one system or format to another system or format. Using more common terms, data engineers query data from a source (extract), they perform some modifications to the data (transform), and then they put that data in a location where users can access it and know that it is production quality (load). The terms extract, transform, and load will be used a lot throughout this...

 

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...

 

Data engineering tools

To build data pipelines, data engineers need to choose the right tools for the job. Data engineering is part of the overall big data ecosystem and has to account for the three Vs of big data:

  • Volume: The volume of data has grown substantially. Moving a thousand records from a database requires different tools and techniques than moving millions of rows or handling millions of transactions a minute.
  • Variety: Data engineers need tools that handle a variety of data formats in different locations (databases, APIs, files).
  • Velocity: The velocity of data is always increasing. Tracking the activity of millions of users on a social network or the purchases of users all over the world requires data engineers to operate often in near real time.

Programming languages

The lingua franca of data engineering is SQL. Whether you use low-code tools or a specific programming language, there is almost no way to get around knowing SQL. A strong foundation...

 

Summary

In this chapter, you learned what data engineering is. Data engineering roles and responsibilities vary depending on the maturity of an organization's data infrastructure. But data engineering, at its simplest, is the creation of pipelines to move data from one source or format to another. This may or may not involve data transformations, processing engines, and the maintenance of infrastructure.

Data engineers use a variety of programming languages, but most commonly Python, Java, or Scala, as well as proprietary and open source transactional databases and data warehouses, both on-premises and in the cloud, or a mixture. Data engineers need to be knowledgeable in many areas – programming, operations, data modeling, databases, and operating systems. The breadth of the field is part of what makes it fun, exciting, and challenging. To those willing to accept the challenge, data engineering is a rewarding career.

In the next chapter, we will begin by setting...

About the Author
  • Paul Crickard

    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.

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Latest Reviews (6 reviews total)
Well written and clear to umderstand.
Excellent reference library
Gr8 !!!!!!!!!!!!!!!!!!!!!!
Data Engineering with Python
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