Top 5 Data Engineering Projects You Can't Afford to Miss

Yusuf Ganiyu

Sun, 06 Apr 2025

The realm of data engineering is undergoing a transformation like never before. With the advent of groundbreaking technologies and innovative methodologies, the ways in which we collect, process, and analyze data are being revolutionized. This dynamic shift presents both challenges and opportunities for professionals in the field. To navigate this evolving landscape successfully, it’s imperative to not only keep pace with these changes but also to master them.

In this article, we delve into five cutting-edge data engineering projects that are at the forefront of today’s trends. These projects are meticulously chosen to not only enhance your technical prowess but also to provide you with the critical insights and skills necessary to excel in this ever-changing environment. Prepare to embark on a journey that will not only challenge your understanding of data engineering but also expand your capabilities and set you apart in this competitive field.

Oh, by the way, if you’re interested in all data engineering contents and courses, you can find them on datamasterylab.com (both free and paid courses).

With that said, let’s dive right in…

1. Kubernetes for Data Engineering

Project Overview:

This project is designed to provide a deep dive into the world of Kubernetes, specifically tailored for data engineering applications. Kubernetes, an open-source platform for automating deployment, scaling, and operations of application containers across clusters of hosts, has become the backbone of container orchestration in the industry. Through this project, participants will gain hands-on experience in leveraging Kubernetes to orchestrate and manage complex, containerized data workflows and applications. The project will cover the deployment of these applications, how to scale them based on demand, and the management of their lifecycle across various environments, from development through to production.

Why It’s Important:

Kubernetes has become the de facto standard for container orchestration, offering scalability, fault tolerance, and portability. Understanding Kubernetes in the context of data engineering enables professionals to build more resilient and scalable data pipelines, catering to the dynamic demands of modern data processing.

Technologies Used:

• Kubernetes: At the heart of this project, Kubernetes will be used for orchestrating containerized applications, managing their lifecycle, scaling, and automated deployment.
• Docker: Essential for creating containerized versions of applications, Docker simplifies the deployment of applications in Kubernetes pods.
• Helm: A package manager for Kubernetes, Helm simplifies the deployment of complex applications by managing Kubernetes charts (packages of pre-configured Kubernetes resources).

2. CI/CD for Modern Data Engineering

Project Overview:

This project focuses on leveraging Terraform, HashiCorp Configuration Language (HCL), and Microsoft Azure to automate the provisioning and management of cloud infrastructure for data engineering workflows. Terraform, an open-source infrastructure as code software tool created by HashiCorp, enables users to define and provision cloud infrastructure using a high-level configuration language known as HCL. By integrating Terraform with Azure, this project aims to streamline the deployment and management of the necessary cloud infrastructure for data engineering projects, ensuring consistency, scalability, and efficiency.

Why It’s Important:

Incorporating CI/CD into data engineering workflows brings several key benefits, in our case, we focus squarely on Terraform however these benefits still apply overall to the fusion of CI/CD into Data Engineering:

• Faster Time to Market: Automating the build and deployment processes significantly reduces the time required to deploy new features and fixes.
• Increased Reliability: Continuous testing helps catch and fix errors early in the development cycle, improving the reliability of data pipelines.
• Enhanced Collaboration: CI/CD encourages more frequent code integrations, making it easier for teams to collaborate and reducing integration problems.
• Better Quality Control: Automated testing and deployment ensure that every change is tested and validated, leading to higher quality data products.

Technologies Used:

• Terraform: Utilized for writing infrastructure as code (IaC) to automate the deployment and management of cloud infrastructure on Azure.
• HashiCorp Configuration Language (HCL): The human-readable language used by Terraform to describe the desired state of cloud infrastructure in a declarative manner.
• Microsoft Azure: The cloud platform where the infrastructure will be provisioned and managed, hosting data engineering workflows and applications.

Note: This course is part of this FULL COURSE, you should definitely consider enrolling for full experience!

3. Building Robust Data Pipelines for Modern Data Engineering

Project Overview:

This project guides you through the comprehensive setup of an end-to-end data engineering pipeline, utilizing a suite of powerful technologies and methodologies. The core technologies featured in this project are Apache Spark, Azure Databricks, and the Data Build Tool (DBT), all hosted on the Azure cloud platform. A key architectural framework underpinning this project is the medallion architecture, which is instrumental in organizing data layers within a data lakehouse. This project aims to illustrate a holistic process encompassing data ingestion into the lakehouse environment, data integration via Azure Data Factory (ADF), and data transformation leveraging both Databricks and DBT.

Why It’s Important:

Building robust data pipelines is foundational to modern data engineering, enabling efficient data ingestion, processing, and analysis. This project illustrates key concepts in data integration, transformation, and storage, ensuring you can handle complex data workflows leverage the representation of Medallion Architecture for data processing.

Technologies Used:

• Apache Spark: A unified analytics engine for large-scale data processing, known for its speed, ease of use, and sophisticated analytics.
• Azure Databricks: A cloud-based big data analytics platform optimized for the Microsoft Azure cloud services platform, offering a collaborative environment for Spark-based analytics.
• Data Build Tool (DBT): A command-line tool that enables data engineers to transform data in their warehouses more effectively by using SQL-based configuration files for defining data models and transformations.
• Azure Data Factory (ADF): A cloud-based data integration service that allows you to create, schedule, and orchestrate your ETL/ELT workflows.

4. Realtime Voting System | End-to-End Data Engineering Project

Project Overview:

In this comprehensive video tutorial, we embark on a journey to construct an end-to-end real-time voting system leveraging the capabilities of cutting-edge big data technologies. The technologies we’ll be focusing on include Apache Kafka, Apache Spark, and Streamlit. This project is designed to simulate a real-world application where votes can be cast and results can be seen in real-time, making it an excellent case study for understanding the dynamics of real-time data processing and visualization.

Fun fact: I ran commentary for live voting happening in the application for a few minutes, you definitely don’t want to miss that! ????

Why It’s Important:

This project stands as a pivotal learning opportunity for several reasons:

• Real-Time Data Processing: In an era where the velocity of data generation and consumption is unprecedented, mastering real-time data processing is invaluable. This project offers a practical, in-depth look at how to handle and analyze data in real time.
• Integration of Big Data Technologies: Learning to integrate Apache Kafka, Apache Spark, and Streamlit provides a robust foundation in big data technology stack, equipping participants with the skills to tackle a wide range of data processing challenges.
• Hands-On Experience: By building a real-world application, participants will navigate through common challenges and solutions in the realm of big data, enhancing their problem-solving skills and technical acumen.
• Interactive Data Visualization: The use of Streamlit to visualize voting results in real time underscores the importance of immediate feedback in modern applications, offering insights into building user-centric data products.

Technologies Used:

• Apache Kafka: A distributed streaming platform that excels at handling real-time data feeds. Kafka will serve as the backbone for data ingestion, efficiently managing the flow of votes into our system.
• Apache Spark: Renowned for its ability to process large datasets quickly, Apache Spark will be used for real-time data processing and analytics, providing insights into voting trends as they develop.
• Streamlit: A powerful tool for creating interactive and visually appealing web applications. Streamlit will enable us to build a user interface where votes can be cast and results viewed in real time, showcasing the immediate impact of user interactions.

5. Realtime Change Data Capture Streaming | End to End Data Engineering Project

Project Overview:

In this video, In this comprehensive video tutorial series, we explore the transformative approach of Change Data Capture (CDC) for realizing real-time data streaming capabilities. CDC is a method used to capture changes made to the data in databases (like inserts, updates, and deletes) and then stream these changes to different systems in real-time. This project is designed to give you a hands-on experience in setting up a CDC pipeline using a robust technology stack that includes Docker, Postgres, Debezium, Kafka, Apache Spark, and Slack. By integrating these technologies, you will build an end-to-end solution that not only captures and streams data changes efficiently but also processes this data and notifies end-users through Slack (optionally) in real-time.

Why It’s Important:

Change Data Capture (CDC) is essential for real-time data integration and processing, allowing businesses to react quickly to data changes. This project provides hands-on experience with CDC, teaching you how to implement efficient data streaming pipelines that can trigger immediate actions or analyses.

Technologies Used:

• Docker: Used to containerize each component of the pipeline, ensuring consistency across different development and production environments.
• Postgres: Acts as the source database from which data changes are captured. It’s widely used for its robustness and reliability.
• Debezium: An open-source distributed platform that provides CDC for a variety of databases including Postgres. It captures row-level changes to the database in real-time and publishes them to Kafka.
• Kafka: A distributed streaming platform that acts as the backbone for messaging, ensuring that data changes are efficiently streamed between systems.
• Apache Spark: Used for processing the streamed data in real-time, allowing for complex transformations, aggregations, and analytics on the data as it flows through the pipeline.
• Slack (optional): Serves as the endpoint for notifications, where processed data insights or alerts can be sent to inform users or trigger actions.

Resources

1. Medium https://medium.com/@yusuf.ganiyu

2. Youtube - https://www.youtube.com/@codewithyu