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Top Data Engineering Projects for Beginners

Data engineering is the practice of developing large-scale data collection, storage, and analysis systems. It covers a wide range of topics and has uses in almost every business. Massive volumes of data can be gathered by organizations, but to make sure that it is in a highly useable shape by the moment it reaches data scientists and analysts, they need the right personnel and the best technology.

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Want to learn data engineering from the basics, here’s a video for you

What is Data Engineering?

The procedure of developing and creating systems that enable users to gather and evaluate unprocessed data from various sources and forms are known as Data Engineering. These technologies facilitate users to discover useful data applications that firms may use to succeed.

What does a Data Engineer do?

A data engineer’s primary goal is to transform the raw data into something valuable and understandable before presenting it to an organization. In addition, they must design, construct, test, mix, manage, and optimize the data using various sources.

They create the systems that will produce this data. The goal of a data engineer is to build data pipelines that operate efficiently. In addition to all of this, they create challenging queries to make the data available. Depending on their company, data engineers may have a different typical day.

Go through these Data Engineer Interview Questions and Answers to excel in your interview.

Top Data Engineering projects you must know

To become an expert data engineer, you need to be familiar with the most significant and interesting technologies in your field. You will gain knowledge of the industry’s entries and breakaways by working on a data engineering project.

Check out the list of data engineering project examples below if you’re new to the field and want to learn more about actual data engineering projects.

Implement data modeling with Cassandra

The creation of projects like these involving data engineering is exciting. Users may access and use huge amounts of information because of Apache Cassandra, an accessible NoSQL database management system.

Its key advantage is that it lets you use data that is dispersed among numerous commodity servers, reducing the chance of failure. One server failing wouldn’t bring down your entire business because your data is scattered across multiple servers.

This is only one of the multiple factors that make Cassandra so well-liked by eminent data experts. High efficiency and scalability also feature.

Building a Data Lake

For those who are just starting in data engineering, this project is fantastic. The need for data lakes is growing in the market, so you can create one and expand your portfolio. The storage of both organized and unstructured data at any size is done in data lakes.

It allows you to store data without structuring it first, so you can add data to the storage without first structuring it. One of the hottest initiatives in data engineering is this one. Since there is no need for alteration while adding information to the data lake, the procedure is simple and permits real-time data inclusion.

A data lake is required for many current and well-liked technologies, such as machine learning and analytics. You may instantly upload a variety of file kinds to your repository using data lakes, and you can fast perform complex operations on the data. Because of this, you should incorporate a data lake into your project and maximize your technological education.

Using Apache Spark on the AWS cloud, you can develop a data lake. You can also conduct ETL operations to enhance data flow throughout the data lake and add interest to the project. Your resume will look far more fascinating than other resumes if you highlight data engineering projects.

Create a Data Warehouse

Constructing a data warehouse is one of the greatest approaches to begin your practical data engineering projects for students. One of the most sought-after talents for data engineers is data warehousing.

For this reason, we advise including the creation of a data warehouse in your data engineering initiatives. This project will assist you in learning how to build a data warehouse and its associated applications.

A data warehouse gathers disparate data from several sources and turns it into a standardized, explicit form. Data warehousing is a crucial part of business intelligence (BI) and aids in the optimal use of information. Various other terms for data warehouses include:

Data warehouses are generally used to assist business analysts with their duties and can store massive amounts of data. On the AWS cloud, you may construct a data warehouse and add an ETL pipeline for transferring and transforming data into the warehouse.

After concluding this assignment, you will be acquainted with almost every facet of data warehousing.

Event Data Analysis

NCR Open Data is accessible public information made available by organizations and authorities in New Delhi City. This project offers data aficionados the chance to interact with the data generated and used by the New Delhi City authority.

You will evaluate the incidents and accidents in New York City. This is a complete big data project for developing a data engineering pipeline that includes event data on the cloud information gathering, filtering, modification, and exploratory analysis as well as data visualization and data flow orchestration.

To retrieve real-time broadcast event data from the NYC city accidents data source, you will investigate various data engineering techniques. The data will be processed on AWS to extract key performance indicators (KPIs), which will then be sent to Elasticsearch for text-based search and analysis using Kibana visualization.

Real-Time Data Analytics

A food service company called Zingy collects data about each food delivery. Per delivery, two different devices generate additional information. The Delivery app sends information about each delivery duration, distance, and pick-up and drop-off locations of the restaurants.

Real-Time Data Analysis Project

Users can pay via a mobile platform, which also transmits information about prices. To identify customer trends, the food delivery company needs to determine the typical tip per km delivered for each location in authentic.

Website Monitoring

The term website monitoring refers to any action that involves evaluating a website or other web service for its functionality, performance, or availability. It involves both testing and confirming that end users can interact with a website or web application with the website. The website monitoring service monitors and confirms that the website is operational and operating as intended and that users can access and navigate it without any issues.

Bitcoin Mining

The development and upkeep of the blockchain ledger depend on bitcoin mining. It is the procedure used to add fresh bitcoins to the network. It is carried out utilizing cutting-edge computers that can handle challenging math issues. You will use data mining ideas in this data engineering project to mine bitcoin using publicly accessible relative data.

Bitcoin Mining Project

This is a simple project where you will use Python to collect data from APIs, process it, and save it manually to EC2 instances. Transmit data to HDFS after that. Then, using Pyspark, access the information from HDFS and carry out the evaluation.

The utilization of Spark optimization algorithms and Kryo serialization are the methods described in this use case. On Hive/Presto, an extra table will be generated, and finally, AWS Quicksight will be used to represent the information.


When choosing a project, the optimal project achieves a balance between the interests of industry and personal interest. It doesn’t matter if you like it or not, the topic you pick reflects your interests, so picking a project you enjoy is vital. You can adapt the projects on the list above to an area of interest to you if your passions are in commodities, real estate, economics, or some other specialized field.

Your queries & doubts will get resolved on Intellipaat’s data science Community Page!

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