For enquiries call:
+1-469-442-0620
HomeBlogData ScienceTop 12 Data Engineering Project Ideas [With Source Code]
Welcome to the world of data engineering, where the power of big data unfolds. If you're aspiring to be a data engineer and seeking to showcase your skills or gain hands-on experience, you've landed in the right spot. Get ready to delve into fascinating data engineering project concepts and explore a world of exciting data engineering projects in this article.
Before working on these initiatives, you should be conversant with topics and technologies. Companies are constantly seeking experienced data engineers who can create innovative data engineering initiatives. Therefore, the greatest thing you can do as a novice is to work on some real-time data engineering initiatives. Working on a data engineering project will not only give you a deeper understanding of how data engineering works, but it will also improve your problem-solving skills as you encounter and fix problems within the project. Best Data Science certifications online or offline are available to assist you in establishing a solid foundation for every end-to-end data engineering project.
If you want to break into the field of data engineering but don't yet have any expertise in the field, compiling a portfolio of data engineering projects may help. From exploratory data analysis (EDA) and data cleansing to data modeling and visualization, the greatest data engineering projects demonstrate the whole data process from start to finish.
Data pipeline best practices should be shown in these initiatives. You should be able to identify potential weak spots in data pipelines and construct robust solutions to withstand them. Finally, make data visualizations to display your project's results and construct a website to showcase your work, whether it's a portfolio or a personal site.
The first step in hiring data engineers is reviewing a candidate's résumé. When screening resumes, most hiring managers prioritize candidates who have actual experience working on data engineering projects.
Data engineers make unprocessed data accessible and functional for other data professionals. Multiple types of data exist within organizations, and it is the obligation of data architects to standardize them so that data analysts and scientists can use them interchangeably. If data scientists and analysts are pilots, data engineers are aircraft manufacturers. Without the latter, the former cannot accomplish its objectives. From analysts to Big Data Engineers, everyone in the field of data science has been discussing data engineering.
When constructing a data engineering project, you should prioritize the following areas:
Becoming an expert data engineer necessitates familiarity with the best practices and cutting-edge technologies in your field. Participating in a data engineering project is a great way to learn the ropes of the field. That's why we're going to zero in on the data engineering initiatives that need your attention. If you are struggling with Data Engineering projects for beginners, then Data Engineer Bootcamp is for you.
Some simple beginner Data Engineer projects that might help you go forward professionally are provided below.
1. Stock and Twitter Data Extraction Using Python, Kafka, and Spark
Project Overview: The rising and falling of GameStop's stock price and the proliferation of cryptocurrency exchanges have made stocks a topic of widespread attention.
If you share this individual's enthusiasm for the markets, you may want to consider creating a tool like Cashtag, which was created by a Reddit developer. For this study, we wanted to create a "big data pipeline for user sentiment analysis on the US stock market." In a nutshell, this initiative uses social media data to provide real-time market sentiment predictions. The process flow for this project is shown in the following diagram:
This project's documentation will serve as a starting point from which you may draw ideas for your own work.
Source Code: Stock and Twitter Data Extraction Using Python, Kafka, and Spark
2. Use Python to Scrape Real Estate Listings and Make a Dashboard
Project Overview: If you're looking to get your hands dirty with some cutting-edge tech and big Data Engineering projects for engineering students, consider something like sspaeti's 20-minute data engineering project. The purpose of this work is to provide a resource that can help you find the best possible home or rental.
Web scraping applications like Beautiful Soup and Scrapy are used to gather information for this project. As a data engineer, you should get experience writing Python programs that process HTML, and web scraping is an excellent method to do so. Delta Lake and Kubernetes are both trending subjects. Therefore it's interesting to see them both addressed in this project.
Finally, a well-designed user interface is an essential part of any successful data engineering project. Superset is used for data visualization in this project, while Dagster is used to coordinate the many moving parts. The wide range of methods used in this work makes it an excellent addition to a resume.
Source: Use Python to Scrape Real Estate Listings and Make a Dashboard
3. Use Stack Overflow Data for Analytic Purposes
Project Overview: What if you had access to all or most of the public repos on GitHub? Which queries do you have?
As part of similar research, Felipe Hoffa analysed gigabytes of data spread over many publications from Google's BigQuery data collection. However, the abundance of data opens numerous possibilities for research and analysis. Concepts that Felipe examined include:
2.8 million open-source projects are available for inspection.
Moreover, this project concept should highlight the fact that there are many interesting datasets already available on services like GCP and AWS. Hundreds of datasets are available from these two cloud services, so you may practise your analytical skills without having to scrape data from an API.
Source: Use Stack Overflow Data for Analytic Purposes
4. Extracting Inflation Rates from CommonCrawl and Building a Model
Project Overview: Dr. Usama Hussain worked on another intriguing idea. He calculated the rate of inflation by following internet pricing fluctuations for products and services. Given that the United States has had the highest inflation rate since 2008, this is a significant problem.
The author utilised petabytes of website data from the Common Crawl in their effort.
This is also another excellent example of putting together and showing a data engineering project, in my opinion. One of the difficulties I often mention is how difficult it may be to demonstrate your data engineering job.
However, Dr. Hussain's project is documented in such a way that it is possible to see what work was done and the skills he possesses without having to dig into all the code.
The data flow is outlined below by Dr. Hussain.
Source Code: Extracting Inflation Rates from CommonCrawl and Building a Model
Knowing big data theory alone will not get you very far. You'll need to put your newfound knowledge into action. Working on big data projects allows you to put your big data skills to the test. Projects are a wonderful way to put your skills to the test. They are also excellent for your resume. This post will go over some amazing Big Data projects that you may work on to demonstrate your big data expertise and these are solid Data Engineering projects for resume.
Here are some data engineering project ideas to consider and Data Engineering portfolio project examples to demonstrate practical experience with data engineering problems.
1. Realtime Data Analytics
Project Overview: Olber, a corporation that provides taxi services, is gathering information about each and every journey. Per trip, two different devices generate additional data. The taxi metre transmits information on the length of each journey, the distance travelled, as well as the pick-up and drop-off locations. Customers' payments are processed using a smartphone application, which also provides reliable and easily accessible data about fares. In order to identify patterns among its customers, the taxi firm needs to compute, in real-time, the typical amount of cash given as a tip for each kilometre travelled in each region.
A complete end-to-end stream processing pipeline is shown here using an architectural diagram. Extracting, transforming, loading, and reporting are the four processes that make up this kind of pipeline. The pipeline in this reference design collects data from two different sources, then conducts a join operation on related records from each stream, then enriches the output, and finally produces an average. The findings are being saved for use in further analyses.
Source Code: Realtime Data Analytics
2. Yelp Review Analysis
Project Overview: Yelp is a platform that allows people to post reviews and provide a star rating to businesses that they have visited. Studies found that a one-star raise led to a 59 percent gain in revenue for independently owned and operated firms. As a consequence of this, we think the Yelp dataset has a lot of promise as a resource for gaining valuable insights. Yelp reviews written by customers are a treasure trove just waiting to be unearthed.
The primary objective of this project is to carry out in-depth analyses of seven different cuisine types of restaurants, namely Korean, Japanese, Chinese, Vietnamese, Thai, French, and Italian, in order to determine what makes a good restaurant and what concerns customers, and then to make recommendations for future improvement and growth in profit. The majority of our focus will be on analysing feedback from consumers to figure out why they either like or detest the company. Using big data, we are able to transform unstructured data, such as customer reviews, into actionable insights, which enables businesses to better understand how and why customers prefer their products or services and to make improvements to their operations as quickly as is practically possible.
Source Code: Yelp Review Analysis
2. Finnhub API with Kafka for Real-Time Financial Market Data Pipeline
Project Overview: The goal of this project is to construct a streaming data pipeline by making use of the real-time financial market data API provided by Finnhub. This project's architecture is essentially composed on five layers: the Data Ingestion layer, the Message broker layer, the Stream processing layer, the Serving database layer, and the Visualisation layer. A dashboard that provides data in a graphical manner for in-depth study is the final product of this project.
The pipeline consists of many different components, one of which is a producer that retrieves data from Finnhub's API and then transmits that data to a Kafka topic, which is part of a Kafka cluster that stores the data and processes it. Apache Spark is going to be used for stream processing. The next step is to use Cassandra for the purpose of storing the real-time financial market data that is being sent over the pipeline. Users are able to watch the market data in real-time and detect trends and patterns by using the final dashboard that was created with the help of Grafana. This dashboard shows real-time charts and graphs that are based on the data that is stored in the database.
Source Code: Finnhub API with Kafka for Real-Time Financial Market Data Pipeline
3. Pipeline for Real-Time Data Processing in Music Applications
Project Overview: The project will stream events that are created by a fictitious music streaming service that operates similarly to Spotify. Additionally, a data pipeline that consumes real-time data will be developed. The incoming data would be analogous to an event that occurred when a person listened to music, navigated around the website, or authenticated themselves. The processing of the data would take place in real-time, and it would be saved to the data lake at regular intervals (every two minutes). The hourly batch job will then make use of this data by consuming it, applying transformations to it, and creating the tables that are needed for our dashboard so that analytics may be generated. We are going to try to conduct an analysis of indicators such as the most played songs, active users, user demographics, etc.
You will be able to generate a sample dataset for this project by using Eventism and the Million Songs dataset. Apache Kafka and Apache Spark are two examples of streaming technologies that are used for processing data in real-time. The Structured Streaming API offered by Spark makes it possible for data to be processed in real-time in mini-batches, which in turn offers low-latency processing capabilities. The processed data are uploaded to Google Cloud Storage, where they are then subjected to transformation with the assistance of dbt. We can clean the data, convert the data, and aggregate the data using dbt so that it is ready for analysis. The data is then sent to BigQuery, which serves as a data warehouse, and Data Studio is used to create a visual representation of the data. Apache AirFlow has been used for the purpose of orchestration, whilst Docker is the tool of choice when it comes to containerization.
Source Code: Pipeline for Real-Time Data Processing in Music Applications
After you have worked on these, adding projects for Data Engineering to your resume will likely increase the likelihood that an interview will be requested of you.
1. Anomaly Detection in Cloud Servers
Project Overview: Anomaly detection is a valuable instrument for cloud platform administrators who wish to monitor and analyse cloud behaviour in order to increase cloud reliability. It aids cloud platform administrators in detecting unanticipated system activity in order to take preventative measures prior to a system breakdown or service failure.
This project provides a reference implementation of a Cloud Dataflow streaming pipeline that integrates with BigQuery ML, Cloud AI Platform, to detect anomalies. A critical component of the implementation utilises Dataflow for feature extraction and real-time outlier detection, which has been validated on over 20TB of data.
Source Code: Anomaly Detection in Cloud Servers
2. Smart Cities Using Big Data
Project Overview: A "smart city" is an ultra-modern urban area that gathers data through electronic means, voice activation techniques, and sensors. The data is used to better manage the city's assets, resources, and services, which in turn leads to better citywide operations. In order to keep tabs on and manage things like traffic and transportation systems, power plants, utilities, water supply networks, waste, crime detection, information systems, educational institutions, health care facilities, and more, data is gathered from citizens, devices, buildings, and assets and then processed and analysed. This data is collected by means of big data, and then the complex characteristics of a smart city may be put into effect with the aid of advanced algorithms, smart network infrastructures, and numerous analytics platforms. For traffic or stadium sensing, analytics, and management, this smart city reference pipeline demonstrates how to combine several media building pieces with analytics provided by the OpenVINO Toolkit.
Source Code: Smart Cities Using Big Data
3. Tourist Behaviour Analysis
Project Overview: One of the most forward-thinking ideas for a big data project is presented here. The purpose of this Big Data project is to research visitor behaviour in order to ascertain the preferences of tourists and the locations that are visited the most, as well as to anticipate the need for tourism in the future.
What part does large amounts of data play in the whole project? Because vacationers use the internet and other technologies while they are away from home, they leave digital traces that can be easily collected and distributed by Big Data. The vast majority of the data comes from outside sources like social media websites. The sheer amount of data is just too much for a conventional database to manage, which is why big data analytics is required. The data collected from all of these sources may be put to use to assist companies in the airline, hotel, and tourism sectors in expanding their client base and marketing their products and services. Additionally, it can assist tourism organisations in visualising and forecasting current and future trends, which is another useful application for the tool.
Source Code: Tourist Behavior Analysis
4. Image Caption Generator
Project Overview: Businesses must now upload engaging content as a result of the rise of social media and the significance of digital marketing. Visuals that are enticing to the eye are essential, but the images must also be accompanied by subtitles. Utilising hashtags and attention-grabbing subtitles may help you reach the intended audience more effectively. Large datasets containing photos and captions that are correlated must be managed. Image processing and deep learning are used to comprehend the image, and artificial intelligence is used to generate relevant and alluring captions. Python source code for Big Data can be written. The creation of image captions is not a Big Data project proposal for beginners and is indeed difficult. Using CNN (Convolution Neural Network) and RNN (Recurrent Neural Network) with BEAM Search, the project described below employs a neural network to generate captions for an image.
Rich and colourful datasets, such as MSCOCO, Flickr8k, Flickr30k, PASCAL 1K, AI Challenger Dataset, and STAIR Captions, are currently used in the generation of image descriptions and are gradually becoming a topic of discussion. The supplied project employs cutting-edge machine learning and big data algorithms to create an efficient image caption generator.
Source Code: Image Caption Generator
Below are some Data Engineering project examples
In conjunction with machine learning, it enables the development of marketing plans that are based on the forecasts of customers. Businesses that use big data analytics become more customer focused.
Learning this skill set, which is in great demand, will allow you to make rapid strides in your professional development. Because of this, the best thing you can do if you're new to big data is to think of some ideas for projects that include big data.
Data engineers are responsible for the construction and administration of computer hardware and software systems that are used for the gathering, formatting, storing, and processing of data. In addition to this, they make sure that the data is always readily accessible to consumers. The end-to-end data process is shown via data engineering projects, which range from exploratory data analysis (EDA) and data cleansing through data modelling and visualisation.
Including Data Engineering projects on your resume is quite crucial if you want your application for a job to stand out from the other applicants who have applied for the same position.
The following is a list of several platforms that are suitable for use in Data Engineering real time projects -
One of the finest data science learning platforms, Google Cloud provides all of the tools that data scientists use to extract value from data, making it one of the top data science learning platforms. Business intelligence solutions like as Power BI, Tableau, and Looker may assist companies in mitigating operational risk and achieving maximum efficiency in terms of operations enablement by assisting businesses in making choices that are supported by data.
A few thing that you should keep in mind while studying for data engineering projects and jobs are -
Elevate your career with business analyst certificate programs . Establish your expertise and open doors to limitless opportunities!
This article examines some of the finest concepts for large data projects. We began with some basic, quick-to-complete assignments and have added Data Engineering projects with source code.
The optimal undertaking is one that establishes a balance between industry interests and personal interests. Whether you like it or not, your personal interest will be communicated through the topic you select, so it is essential to select a topic that you enjoy. If you have an interest in equities, real estate, politics, or any other niche category, you can use the projects listed above as a template for your own project. Checkout KnowledgeHut’s best Data Science certification online for Data Engineering project ideas.
An online portfolio is the best way to showcase your work. Document each project's construction and operation. Your blog entries or Github repositories may show your problem description, recommended design, data analysis approach, and results. Adding real world Data Engineering projects is a good way to showcase projects for Data Engineering.
Start with a question. Next, find a relevant dataset. Kaggle, FiveThirtyEight, Google Trends, the Census Bureau, and Data.gov provide free datasets. Use an open API or web scraping tools to get website data.
project-worthy topics in data engineering:
Data engineering creates a trustworthy data storage and processing infrastructure. Building and maintaining data pipelines to centralize data sources. Data engineers build and maintain the infrastructure data scientists and analysts utilize to work with data.
Data engineering example:
Businesses want to know how website visitors behave. Web logs, smartphone apps, and social media accounts provide data. Databases, JSON, and CSV files contain the data. This data must be collected, normalized, imported into a central data repository, and examined. Data engineering is fascinating. Data engineers take data from multiple sources, convert it to Parquet or ORC, then put it into a data warehouse like Amazon Redshift or Google BigQuery. Data scientists and analysts may then study the data in a data warehouse.
| Name | Date | Fee | Know more |
|---|
![Top 12 Data Engineering Project Ideas [With Source Code]](/_next/image?url=https%3A%2F%2Fd2o2utebsixu4k.cloudfront.net%2Fmedia%2Fimages%2F6dca8111-0644-4b70-b7d1-7ef64dc6b2ef.png&w=1920&q=75)
