{"id":17096,"date":"2022-03-31T15:11:34","date_gmt":"2022-03-31T22:11:34","guid":{"rendered":"https:\/\/www.springboard.com\/blog\/?p=17096"},"modified":"2025-01-27T04:54:49","modified_gmt":"2025-01-27T12:54:49","slug":"data-engineering-projects","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/data-engineering-projects\/","title":{"rendered":"7 Data Engineering Projects to Level Up Your Skills in 2025"},"content":{"rendered":"\n

While data science has been hailed as the \u201csexiest job of the 21st century,\u201d it\u2019s not necessarily the only lucrative job working with data. On average, data engineers actually make $10,000 more than data scientists, and in recent years, data engineering has become the fastest-growing tech occupation. Data engineers plan, build, and maintain the backend infrastructure that enables analytics and data science professionals to extract insights from data. <\/p>\n\n\n\n

If you\u2019re looking to land a job in this promising industry, but don\u2019t know where to start, then data engineering projects are the best way to demonstrate your skills to prospective employers. Keep reading to learn more about project ideas, where to find datasets, and how to promote your projects during the interview process.<\/p>\n\n\n\n

What Is the Point of a Data Engineering Project?<\/h2>\n\n\n\n

If you\u2019re looking for a data engineering job, but don\u2019t yet have any experience as a data engineer, then a portfolio of data engineering projects is a great way to land your first role. The best data engineering projects showcase the end-to-end data process, from exploratory data analysis (EDA) and data cleaning to data modeling and visualization. <\/p>\n\n\n\n

In these projects, make sure that you show evidence of data pipeline best practices. You should be able to spot failure points in data pipelines and build systems that are resistant to failure. Finally, create data visualizations to show the outcome of your project, and build a dedicated website to host your project, be it a portfolio or personal website<\/a>. <\/p>\n\n\n\n

Data Engineering Project Ideas<\/h2>\n\n\n\n

Analytics Application<\/strong><\/h3>\n\n\n\n

Analysis projects involve parsing large datasets for patterns, anomalies, and other insights. You can analyze a variety of data inputs, such as numbers, text, or audio. <\/p>\n\n\n\n

Sentiment analysis<\/a> (AKA \u201copinion mining\u201d) is the use of natural language processing (NLP) to discover how people feel about a product, public figure, or political party. <\/p>\n\n\n\n

Related Read: 9 NLP Project Ideas for Beginners<\/a><\/em><\/p>\n\n\n\n

Social media posts are ripe for this kind of analysis. You can obtain tweets from Twitter about a trending topic or hashtag using the Apache NiFi GetTwitter<\/a> processor\u2014which obtains real-time tweets and ingests them into a messaging queue\u2014or use Twitter\u2019s Recent Search Endpoint<\/a>. <\/p>\n\n\n\n

Once you\u2019ve obtained your dataset, you can determine sentiment scores using Microsoft Azure\u2019s Text Analytics Cognitive Service<\/a>. You can then visualize the results using Python\u2019s Plotly and Dash libraries, similar to what this Github user<\/a> did. <\/p>\n\n\n\n

Extract, Transform, Load (ETL)<\/strong><\/h3>\n\n\n\n

Extract, Transform, Load (ETL) is the process of extracting data from its original source, preparing the data for analysis, and loading it into a target database. Most ETL tools can perform all three steps. <\/p>\n\n\n\n

Building an ETL project shows you are familiar with the end-to-end data engineering process, from extracting and processing data to analyzing and visualizing data. One popular project is to build a data pipeline that ingests real-time sales data. Using this data pipeline, you can analyze sales metrics such as:<\/p>\n\n\n\n