Organizations today are coming to terms with the value that data-driven operations bring to the business. All data-driven organizations need a framework that will facilitate a streamlined, automated, and scalable transition of data through the extraction, transformation, validation, and loading processes before its analysis and visualization using various techniques. This framework, known as a data pipeline, is designed and built by a data engineer. Thus, the ability to design and build data pipelines that overcome latency and meet the business analytical requirements becomes the most sought-after data engineering skill. Also, a data engineer should be able to design and build data warehouses.
Because the data engineering role is becoming increasingly important, completing the data engineer training may not be enough to demonstrate your skills and knowledge. You need a strong portfolio of projects that showcase your skills in the following:
- Design and use of API
- ETL (Extract Transform Load) solutions
- Data cleaning
- Data exploration
- Data scraping
- Data visualization
- SQL
- Python and/or other big data programming languages
- DAGs
- Version management/control
- Data pipeline concepts
Best data engineering beginner projects
While the list is not exhaustive, these are basic skills that can help you design innovative data engineering solutions. Working on projects helps you to identify your strengths and weaknesses while also gaining some exposure to real-world experience. Based on these fundamental skills, here are data engineering projects that you can work on as a beginner to build a strong portfolio.
1. Data pipeline concepts with Apache Airflow
Apache Airflow is an open-source workflow management platform designed to automate and schedule complex workflows. It has been widely implemented for managing data pipelines.
In this project, you will develop a production-grade data pipeline and organize its workflow using Apache Airflow. You will learn how to schedule and automate ETL processes and create custom project-specific plugins and operators.
2. Data streaming using Kafka
This project helps you to hone your stream processing skills by building a real-time stream processing data pipeline for the Chicago Transit Authority (CTA) that displays the current status of its systems for its commuters. You will extract CTA’s data from its POSTGRES database to feed into the dashboard that will display the system status of its commuters.
3. Insight data engineering with Twitter
This is a coding challenge on GitHub. You will develop primitive features to analyze Twitter users. The two features that you need to implement include a feature that cleans and extracts text from the JSON tweets in Twitter’s streaming API. The next tool you will develop in this project is a feature that calculates the average degree of a vertex in a Twitter hashtag graph every 60 seconds and updates every time a new tweet is posted.
4. Data Lakes with Apache Spark
In this project, you will develop an ETL pipeline for a data lake that will extract data from S3, use Apache Spark to process it, and load the data back into S3 after organizing it into dimensional tables. This is useful to data scientists as it helps them draw insights from the data lake. You will be required to write Python scripts, use PySpark for data wrangling, design a star schema for the data and load it back into S3 as dimensional files.
5. Anomaly detection
The anomaly detection project on GitHub will help you to learn how to build a real-time platform for analyzing the purchases within a social network of users to detect behavior that is far from average in the social network. Ecommerce sites nowadays have social networks where their buyers interact and are able to see and be influenced by what their friends are buying. Developing this code helps to discover abnormal consumer behavior to give insight into their purchasing trends.
6. API to Postgres
In this project, you will build an ETL pipeline to extract real-time data from an open-source API and store it in a database. The open-source API used in this case is the Yelp FUSION API, and the database to be used is PostgreSQL. PostgreSQL is a massive open-source database that drives applications.
Conclusion
Most of the data engineering projects we have listed in this article are publicly available on GitHub. You can explore many other projects on GitHub depending on the skills that you wish to reflect in your portfolio. All in all, a project portfolio remains to be one of the most effective ways of demonstrating your skills and landing that dream data engineering position.
Data engineers play the all-important role of designing the pipelines and architecture required to extract both data from various sources, process, and structure the data in databases for data scientists to draw insights and hidden trends that are crucial for data-driven decision-making in businesses. Without an effective framework for a data pipeline, a business cannot analyze data effectively.