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

Understanding how Kafka uses logs

Kafka maintains logs that are written to by producers and read by consumers. The following sections will explain topics, consumers, and producers.


Apache Kafka uses logs to store data – records. Logs in Kafka are called topics. A topic is like a table in a database. In the previous chapter, you tested your Kafka cluster by creating a topic named dataengineering. The topic is saved to disk as a log file. Topics can be a single log, but usually they are scaled horizontally into partitions. Each partition is a log file that can be stored on another server. In a topic with partitions, the message order guarantee no longer applies to the topic, but only each partition. The following diagram shows a topic split into three partitions:

Figure 13.2 – A Kafka topic with three partitions

The preceding topic – Transactions – has three partitions labeled P1, P2, and P3. Within each partition, the...