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)
1
Section 1: Building Data Pipelines – Extract Transform, and Load
8
Section 2:Deploying Data Pipelines in Production
14
Section 3:Beyond Batch – Building Real-Time Data Pipelines

Installing and configuring Elasticsearch

Elasticsearch is a search engine. In this book, you will use it as a NoSQL database. You will move data both to and from Elasticsearch to other locations. To download Elasticsearch, take the following steps:

  1. Use curl to download the files, as shown:
    curl https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.0-darwin-x86_64.tar.gz --output elasticsearch.tar.gz
  2. Extract the files using the following command:
    tar xvzf elasticsearch.tar.gz
  3. You can edit the config/elasticsearch.yml file to name your node and cluster. Later in this book, you will set up an Elasticsearch cluster with multiple nodes. For now, I have changed the following properties:
    cluster.name: DataEngineeringWithPython 
    node.name: OnlyNode
  4. Now, you can start Elasticsearch. To start Elasticsearch, run the following:
    bin/elasticsearch
  5. Once Elasticsearch has started, you can see the results at http://localhost:9200. You should see the following output...