Top 10 Exciting Data Engineering Projects & Ideas For Beginners [2024]

Data engineering is an exciting and rapidly growing field that focuses on building, maintaining, and improving the systems that collect, store, process, and serve data. As more companies realize the value of their data, there is an increasing demand for data engineers who can design and implement data pipelines, warehouses, lakes, and other significant data infrastructures. If you’re interested in getting into data engineering or want to level up your skills with hands-on practice, taking on a few data projects is great. In this blog, I’ll share some of my favorite data engineering projects ideas suitable for beginners and more experienced engineers. 

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You should note that you should be familiar with some topics and technologies before you work on these projects. Companies are always on the lookout for skilled data engineers who can develop innovative data engineering projects. So, if you are a beginner, the best thing you can do is work on some real-time data engineering projects. Working on a data engineering project will not only give you more insight into how data engineering works but will also strengthen your problem-solving skills when you encounter bugs inside the project and debug them yourself.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting data engineering projects which beginners can work on to put their data engineering knowledge to test. In this article, you will find top data engineering projects for beginners to get hands-on experience. If you are a beginner and interested to learn more about data science, check out our data analytics courses from top universities.

Amid the cut-throat competition, aspiring Developers must have hands-on experience with real-world data engineering projects. In fact, this is one of the primary recruitment criteria for most employers today. As you start working on data engineering projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career.

That’s because you’ll need to complete the projects correctly. Here are the most important ones:

  • Python and its use in big data
  • Extract Transform Load (ETL) solutions
  • Hadoop and related big data technologies
  • Concept of data pipelines
  • Apache Airflow

Also Read: Big Data Project Ideas

What is a Data Engineer?

Data engineers make raw data usable and accessible to other data professionals. Organizations have multiple sorts of data, and it’s the responsibility of data engineers to make them consistent, so data analysts and scientists can use the same. If data scientists and analysts are pilots, then data engineers are the plane-builders. Without the latter, the former can’t perform its tasks. Data engineering topics have been word of mouth everywhere in the domain of data science, from an analyst to a Big Data Engineer.

Data engineers play a pivotal role in the data ecosystem, acting as the architects and builders of infrastructure that enables data analysis and interpretation. Their expertise extends beyond data collection and storage, encompassing the intricate task of transforming raw, disparate data into a harmonized and usable format. By designing robust data pipelines, data engineers ensure that data scientists and analysts have a reliable and structured foundation to conduct their analyses.

These professionals possess a deep understanding of data manipulation tools, database systems, and programming languages, allowing them to orchestrate the seamless flow of information across various platforms. They implement strategies to optimize data retrieval, processing, and storage, accounting for scalability and performance considerations. Moreover, data engineers work collaboratively with data scientists, analysts, and other stakeholders to comprehend data requirements and tailor solutions accordingly.

Essentially, data engineers are the architects of the data landscape, laying the groundwork for actionable insights and informed decision-making. As the data realm continues to evolve, the role of data engineers remains indispensable, ensuring that data flows seamlessly, transforms meaningfully, and empowers organizations to unlock the true potential of their data-driven endeavors.

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Skills you need to become a Data Engineer

As a Data Engineer you have to work on raw data and perform certain tasks on the data. Some tasks of a data engineer are:

  • Acquiring and sourcing data from multiple places
  • Cleaning the data and get rid of useless data & errors
  • Remove any duplicates present in the sourced data
  • Transform the data into the required format

To become a proficient data engineer, you need to acquire certain skills. Here’s a list and a bit about each skill that will help you become a better data engineer:

  • Coding skills: Most data engineering jobs nowadays need candidates with strong coding skills. Numerous job postings stipulate a minimum requirement of applicants’ familiarity with a programming language, often one of the popular coding languages such as Scala, Perl, Python, Java, etc.
  • DBMS: Engineers working with data should be well-versed in all things related to database administration. An in-depth understanding of Structured Query Language (SQL) is crucial in this profession since it is the most popular choice. SQL stands for Structured Query Language and retrieves and manipulates information in a database table. If you want to succeed as a data engineer, learning about Bigtable and other database systems is essential.
  • Data Warehousing: Data engineers are responsible for managing and interpreting massive amounts of information. Consequently, it is essential for a data engineer to be conversant with and have expertise with data warehousing platforms like Redshift by AWS.
  • Machine Learning: Machine learning is the study of how machines or computers may “learn” or use information gathered from previous attempts to improve their performance on a given task or collection of activities.Though data engineers do not directly work on creating or designing machine learning models. It is their job to create the architecture on which Data Scientists and Machine Learning Engineers apply their models. Hence, a knowledge of Machine Learning is essential for a Data Engineer.
  • Operating Systems, Virtual Machines, Networking, etc.

As the demand for big data is increasing, the need for data engineers is rising accordingly. Now that you know what a data engineer does, we can start discussing our data engineering projects. 

Let’s start looking for data engineering projects to build your very own data projects!

So, here are a few data engineering projects which beginners can work on:

Data Engineering Projects You Should Know About

To become a proficient data engineer, you should be aware of your sector’s latest and most popular tools. Working on a data engineer project will help you know the ins and outs of the industry. That’s why we’ll focus on the data engineering projects you should be mindful of:

1. Prefect

Prefect is a data pipeline manager through which you can parametrize and build DAGs for tasks. It is new, quick, and easy-to-use, due to which it has become one of the most popular data pipeline tools in the industry. Prefect has an open-source framework where you can build and test workflows. The added facility of private infrastructure enhances its utility further because it eliminates many security risks a cloud-based infrastructure might pose. 

Even though Prefect offers a private infrastructure for running the code, you can always monitor and check the work through their cloud. Prefect’s framework is based on Python, and even though it’s entirely new in the market, you’d benefit greatly from learning Prefect. Taking up a data engineering project on Prefect will be convenient for you due to the resources available on the internet, being an open-source framework.

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2. Cadence

Cadence is a fault-tolerant coding platform that gets rid of many complexities of building distributed applications. It secures the complete application state that allows you to program without worrying about the scalability, availability, and durability of your application. It has a framework as well as a backend service. Its structure supports multiple languages, including Java and Go. Cadence facilitates horizontal scaling along with a replication of past events. Such replication enables easy recovery from any sorts of zone failures. As you would’ve guessed by now, Cadence is undoubtedly a technology you should be familiar with as a data engineer. Using Cadence for a data engineer project will automate a lot of mundane tasks that you would otherwise need to perform to build your own data engineer project from scratch.

3. Amundsen

Amundsen is a product of Lyft and is a metadata and data discovery solution. Amundsen offers multiple services to users that make it a worthy addition to any data engineer’s arsenal. The metadata service, for example, takes care of the metadata requests of the front-end. Similarly, it has a framework called data builder to extract metadata from the required sources. Other prominent components of this solution are the search service, the library repository named Common, and the front-end service, which runs the Amundsen web app. 

4. Great Expectations

Great Expectations is a Python library that lets you validate and define rules for datasets. After determining the rules, validating data sets becomes easy and efficient. Moreover, you can use Great Expectations with Pandas, Spark, and SQL. It has data profilers that can produce automated expectations, along with clean documentation for HTML data. While it’s relatively new, it is certainly gaining popularity among data professionals. Great Expectations automates the verification process for new data you receive from other parties (teams and vendors). It saves a lot of time in data cleaning, which can be a very exhaustive process for any data engineer. 

Must Read: Data Mining Project Ideas

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Data Engineering Project Ideas You can Work on

This list of data engineering projects for students is suited for beginners, intermediates & experts. These data engineering projects will get you going with all the practicalities you need to succeed in your career.

Further, if you’re looking for data engineering projects for final year, this list should get you going. If you are keen on data engineering and want to write your final year thesis on data engineering topics, then you should definitely start looking up data engineering research topics online without any delay. So, without further ado, let’s jump straight into some data engineering projects that will strengthen your base and allow you to climb up the ladder.

Here are some data engineering project ideas that should help you take a step forward in the right direction and strengthen your profile as a project data engineer.

1. Build a Data Warehouse

One of the best ideas to start experimenting you hands-on data engineering projects for students is building a data warehouse. Data warehousing is among the most popular skills for data engineers. That’s why we recommend building a data warehouse as a part of your data engineering projects. This project will help you understand how you can create a data warehouse and its applications.

A data warehouse collects data from multiple sources (that are heterogeneous) and transforms it into a standard, usable format. Data warehousing is a vital component of Business Intelligence (BI) and helps in using data strategically. Other common names for data warehouses are:

  • Analytic Application
  • Decision Support System
  • Management Information System

Data warehouses are capable of storing large quantities of data and primarily help business analysts with their tasks. You can build a data warehouse on the AWS cloud and add an ETL pipeline to transfer and transform the data into the warehouse. Once you’ve completed this project, you’d be familiar with nearly all aspects of data warehousing. 

2. Perform Data Modeling for a Streaming Platform

One of the best ideas to start experimenting you hands-on data engineering projects for students is performing data modeling. In this project, a streaming platform (such as Spotify or Gaana) wants to analyze its user’s listening preferences to enhance their recommendation system. As the data engineer, you have to perform data modeling so they can explain their user data adequately. You’ll have to create an ETL pipeline with Python and PostgreSQL. Data modeling refers to developing comprehensive diagrams that display the relationship between different data points. 

Some of the user points you would have to work with would be:

  • The albums and songs the user has liked
  • The playlists present in the user’s library
  • The genres the user listens to the most
  • How long the user listens to a particular song and its timestamp

Such information would help you model the data correctly and provide an effective solution to the platform’s problem. After completing this project, you’d have ample experience in using PostgreSQL and ETL pipelines. 

3. Build and Organize Data Pipelines

If you’re a beginner in data engineering, you should start with this data engineering project which is one of the best data engineering research topics. Our primary task in this project is to manage the workflow of our data pipelines through software. We’re using an open-source solution in this project, Apache Airflow. Managing data pipelines is a crucial task for a data engineer, and this project will help you become proficient in the same.

Apache Airflow is a workflow management platform and started in Airbnb in 2018. Such software allows users to manage complex workflows easily and organize them accordingly. Apart from creating workflows and managing them in Apache Airflow, you can also build plugins and operators for the task. They will enable you to automate the pipelines, which would reduce your workload considerably and increase efficiency. Automation is one of the key skills required in the IT industry, from Data Analytics to Web/ Android Development. Automating pipelines in a project will surely give your resume the upper hand when applying as a project data engineer.

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4. Create a Data Lake 

This is an excellent data engineering projects for beginners. Data lakes are becoming more critical in the industry, so you can build one and enhance your portfolio. Data lakes are repositories for storing structured as well as unstructured data at any scale. They allow you to store your data as-is, i.e., and you don’t have to structure your data before adding it to the storage. This is one of the trending data engineering projects. Because you can add your data into the data lake without needing any modification, the process becomes quick and allows real-time addition of data.

Many popular and latest implementations such as machine learning and analytics require a data lake to function correctly. With data lakes, you can add multiple file-types in your repository, add them in real-time, and perform crucial functions on the data quickly. That’s why you should build a data lake in your project and learn the most about this technology.

You can create a data lake by using Apache Spark on the AWS cloud. To make the project more interesting, you can also perform ETL functions to better transfer data within the data lake. Mentioning data engineering projects can help your resume look much more interesting than others.

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5. Perform Data Modeling Through Cassandra

This is one of the interesting data engineering projects to create. Apache Cassandra is an open-source NoSQL database management system that enables users to use vast quantities of data. Its main benefit is it allows you to use the data spread across multiple commodity servers, which mitigates the risk of failure. Because your data is spread across various servers, one server’s failure wouldn’t cause your entire operation to shut down. This is just one of the many reasons why Cassandra is a popular tool among prominent data professionals. It also offers high scalability and performance. 

In this project, you’d have to perform data modelling by using Cassandra. However, when modelling data through Cassandra, you should keep a few points in mind. First, make sure that your data is spread evenly. It is one of the trending data engineering projects. While Cassandra helps in ensuring an even spread of your data, you’d have to double-check this for surety. 

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Secondly, use the smallest amount of partitions the software reads while modelling. That’s because a high number of reading partitions would put an added load on your system and hamper overall performance. After finishing this project, you’d be familiar with multiple features and applications of Apache Cassandra. 

Apart from the ones mentioned here, you can also choose to take up projects about data engineering examples used in the real world. Here’s a list of some other projects on data engineering examples:

  • Event Data Analysis
  • Aviation Data Analysis
  • Forecasting Shipping and Distribution Demand
  • Smart IoT Infrastructure

6. IoT Data Aggregation and Analysis

The IoT Data Aggregation and Analysis project involves constructing a robust and scalable data pipeline to collect, process, and derive valuable insights from several Internet of Things (IoT) devices. The objective is to create a seamless data flow from sensors, smart devices, and other connected endpoints into a centralized repository. This repository serves as the foundation for further analysis and visualization.

The project encompasses several key components, starting with a data ingestion system design capable of handling real-time data streams. Efficient data storage, utilizing databases optimized for time-series data, is essential to accommodate the high influx of information. Preprocessing steps are data cleansing, transformation, and enrichment to ensure data quality and consistency.

For analysis, various techniques such as anomaly detection, pattern recognition, and predictive modeling can uncover meaningful insights. These insights might include identifying operational inefficiencies, predicting maintenance needs, or understanding usage patterns.

Ultimately, the project aims to empower stakeholders with actionable insights through interactive dashboards, reports, and visualizations. By successfully executing this project, one gains a deep understanding of data engineering principles, real-time processing, and the complexities of managing diverse IoT data sources.

7. Real-time Data Streaming Pipeline 

This project aims at developing a resilient real-time data streaming pipeline capable of receiving and processing data as it comes. This includes picking the right technology stack, for example Apache Kafka, in order to handle data streams effectively. Scalability in horizontal direction is one of the basic principles of architecture in order to cover the growing data volumes and fault tolerance of the continuous data processing.   

Some of the solutions for the real-time analytics and storage, Apache Flink or Apache Spark Streaming, for instance, can be integrated to derive meaningful insights from the streaming data. 

8. Data Lake Architecture  

Data lake construction framework assumes development of a single storage place that can accommodate large amounts of both structured and unstructured data. This project calls for selecting good storage solutions such as Apache Hadoop Distributed File System (HDFS), cloud-based alternatives like AWS S3 or Google Cloud Storage.   

The architecture should allow for smooth data integration coming from different sources and ensure data organization and metadata management. Security measures of data lake implementation are important for the protection of sensitive information contained in it. 

9. Automated Data ETL (Extract, Transform, Load) Pipeline 

The objective of this data engineer project’s idea is to automate the process of retrieving data from different sources, transforming it into a uniformed format and loading it into a dedicated storage or data warehouse. Such ETL workflows can be orchestrated by tools such as Apache NiFi, Apache Airflow, or Talend. The project entails building efficient data transformation scripts, making sure that data quality checks are incorporated, and enabling these workflows to be scheduled or triggered upon setting predefined conditions or events. 

10. Data Quality Monitoring System  

Designing a data quality monitoring system implies deploying checks and validations throughout the complete data pipeline to guarantee the quality and reliability of the data. This encompasses checks for completeness, verifications of accuracy, and confirmations of consistency. An alerts system is embedded to alert the stakeholders in real time when anomalies or deviations from set data quality standards are identified. The project helps to sustain a high-quality of data that is vital for reliable analytics and decision-making. 

Learn More about Data Engineering

These are a few data engineering projects that you could try out!

Now go ahead and put to test all the knowledge that you’ve gathered through our data engineering projects guide to build your very own data engineering projects!

Becoming a data engineer is no easy feat; there are many topics one has to cover to become an expert. However, if you’re interested in learning more about big data and data engineering, you should head to our blog. There, we share many resources (such as this one) regularly. 

If you’re interested to learn python & want to get your hands dirty on various tools and libraries, check out Executive PG Program in Data Science.

We hope that you liked this article. If you have any questions or doubts, feel free to let us know through the comments below.

As a data engineer, what challenges will you face?

Data engineering is a totally new, level-headed, and dynamic concept that is constantly evolving. As a learner, you might not find courses that will guide you through the nitty-gritty of the subject. Therefore, you will need to learn data engineering on your own, either by practical exposure or by practising in a job. Working with gargantuan data is the everyday job of a data engineer, and this load of data rapidly keeps increasing. Thus, things could get a little out of hand and could get you into trouble if you mess with data. Furthermore, it is tiresome to manage existing pipelines and keep them in order, and with the rise in demand for data pipelines, it is going to be a challenge for data engineers.

What is the difference between Data Governance and Data Engineering?

Data governance stretches emphasis on data administration, whereas data engineering focuses on data execution. Data governance is vast, and data engineers are a part of it. Moreover, data governance has a lot to offer than just data curation. It is impossible for an organisation to have an effective data governance strategy without data engineers to actually implement it.

Why should you consider Data Engineering?

A career in data engineering could be both challenging and rewarding. As a data engineer, you will greatly contribute to the success of the organisation. This data will be further used by scientists, engineers, and analysts. Furthermore, as a data engineer, you also need to put your problem-solving skills to use. It is safe to say that as long as data exists, the demand for data engineers will continuously rise. Moreover, several reports have indicated that data engineering is amongst the most trending jobs now in the market. Additionally, as a data engineer, there are plenty of benefits such as a good salary, career growth, etc. Using the data project ideas, you can build your own projects and put your knowledge to use.

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