Tutorial – Creating an ETL Pipeline in AWS

In today’s cloud-based landscape, Amazon Web Services (AWS) offers a suite of tools that allows data engineers to build robust, scalable, and efficient ETL pipelines. In the previous chapter, we introduced you to some of AWS’s most common resources within its platform, as well as set up your local environment for development with AWS tools. This chapter will guide you through the process of leveraging these tools, illustrating how to architect and implement an effective ETL pipeline in the AWS environment. We will walk you through the creation of a deployable ETL pipeline in Python Lambda Functions and AWS Step Functions. Finally, we’ll create a scalable pipeline using Bonobo, EC2, and RDS. These tools will help all of your data pipelines harness the power of the cloud.

The chapter will cover the following topics:

• Creating a Python pipeline with AWS Lambda and Step Functions:
  • Setting up the AWS CLI in...

Technical requirements

To effectively utilize the resources and code examples provided in this chapter, ensure that your system meets the following technical requirements:

• Software requirements:
  • Integrated development environment (IDE): We recommend using PyCharm as the preferred IDE for working with Python, and we might make specific references to PyCharm throughout this chapter. However, you are free to use any Python-compatible IDE of your choice.
  • Jupyter Notebooks should be installed.
  • Python version 3.6 or higher should be installed.
  • Pipenv should be installed for managing dependencies.
• GitHub repository: The associated code and resources for this chapter can be found in this book’s GitHub repository at https://github.com/PacktPublishing/Building-ETL-Pipelines-with-Python. Fork and clone the repository to your local machine.

Creating a Python pipeline with Amazon S3, Lambda, and Step Functions

In this section, we will create a simple ETL pipeline using AWS Lambda and Step Functions. AWS Lambda is a serverless compute service that allows you to run code without provisioning or managing servers, while Step Functions provides a way to orchestrate the serverless lambda functions and other AWS services into workflows.

Setting the stage with the AWS CLI

Click into the chapter_10 directory of this book’s GitHub repository in your local PyCharm environment. Within the PyCharm terminal, run the following command to configure the AWS CLI:

(Project) usr@project % aws configure

You will then be prompted to enter your access key ID, secret access key, default region name, and default output format. Use your internet browser to log in to your AWS management console to get the following credentials:

AWS Access Key ID [None]: <YOUR ACCESS KEY ID HERE>AWS Secret Access Key [None]: <YOUR SECRET...

An introduction to a scalable ETL pipeline using Bonobo, EC2, and RDS

Extract, Transform, and Load (ETL) pipelines play a crucial role in data processing, enabling organizations to move data from multiple sources, process it, and load it into a data warehouse or other target system for analysis. However, as data volumes grow, so does the need for scalable ETL pipelines that can handle large amounts of data efficiently.

Amazon EC2 is a cloud service that provides virtual computing resources on-demand, offering a scalable and reliable platform to run various types of applications, including web servers, databases, and machine learning models. Amazon RDS is a fully managed relational database service that can be flexibly managed in the cloud, providing a scalable and reliable platform to run large database workloads.

When combined with an ETL-specific Python module such as Bonobo, Amazon EC2 and RDS can be leveraged to create an easily scalable data pipeline. This approach enables...

Summary

In this chapter, we’ve taken a comprehensive journey of how to create both simple and scalable ETL pipelines with AWS, using both the AWS CLI for local development and the AWS management console to use the GUI interface. At first pass, the AWS environment can be a bit overwhelming, but with just a little practice, you’ll start to feel a familiar flow between Amazon resources.

In Chapter 11, we will introduce the use of CI/CD pipelines, specifically tailored for ETL processes. We’ll discuss the significance of CI/CD pipelines in automating code deployment and enhancing efficiency, reliability, and speed in your ETL process. You’ll learn about AWS CodePipeline, AWS CodeDeploy, and AWS CodeCommit and how these services work together to create a robust and automated CI/CD pipeline for your ETL jobs.