Data engineering projects are complex and require careful planning and collaboration between teams. To ensure the best results, it's essential to have clear goals and a thorough understanding of how each component fits into the larger picture.

While many tools are available to help data engineers streamline their workflows and ensure that each element meets its objectives, providing everything works as it should is still time-consuming.

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What Is Data Engineering?

Data engineering is transforming data into a format that other technologies can use. It often involves creating or modifying databases and ensuring that the data is available when needed, regardless of how it was gathered or stored.

Data engineers are responsible for analyzing and interpreting research results, then using those results to build new tools and systems that will support further research in the future. 

They may also play a role in helping to create business intelligence applications by developing reports based on data analysis.

Data Engineering Projects for Beginners

1. Smart IoT Infrastructure

As the IoT continues to expand, the amount of data ingested with high velocity is growing at an alarming rate. It creates challenges for companies regarding storage, analysis, and visualization. 

In this project, we will build a fictitious pipeline network system called SmartPipeNet. SmartPipeNet aims to monitor pipeline flow and react to events along various branches to give production feedback, detect and reactively reduce loss, and avoid accidents.

The architecture shows that simulated sensor data is ingested from MQTT to Kafka.

A column store called HBase stores the Kafka data and analyzes it with Spark Streaming API. Finally, a Java-based custom dashboard is used to publish and visualize the data.

2. Aviation Data Analysis

The airline industry is competitive, and airlines constantly look for ways to improve their services.

One way to do that is by better understanding their customers, who they are, where they're going, and why they're going there.

This data engineering project will help you gather streaming data from Airline API using NiFi and batch data using AWS redshift using Sqoop. Then you'll build a data engineering pipeline to analyze the data using Apache Hive and Druid to analyze the data. Finally, we'll compare the performances of both methods to discuss hive optimization techniques and visualize the data using AWS Quicksight.

3. Shipping and Distribution Demand Forecasting

The demand forecast data engineering project attempts to predict future demand for products, customers, or destinations. This project involves examining historical demand data and applying statistical models to predict future direction. 

A real-world use case for this type of data engineering project is when a logistics company wants to know how many products each customer will order at each location in the future. Using demand forecasts as input for an allocation tool, the company can optimize operations such as delivery vehicle routing and planning capacity in the longer term.

A related example is when a vendor or insurer wants to know how many products will be returned because of failures.

4. Event Data Analysis

The City of New York, publishes a ton of data about its activities and the city itself, but you need help finding the information you need. This project is an opportunity for data enthusiasts to engage in the report produced and used by the New York City government. 

You will analyze accidents happening in NYC, using information from the NYC Open Data portal to build a data engineering pipeline involving data extraction, data cleansing, transformation, exploratory analysis, data visualization, and data flow orchestration of event data on the cloud.

You'll start by exploring various data engineering processes to extract real-time streaming event data from the NYC city accidents dataset. Then you'll process this data on AWS to extract KPIs that will eventually be pushed to Elasticsearch for text-based search and analysis using Kibana visualization.

5. Data Ingestion 

Data ingestion is moving data from one or more sources to a target site for further processing and analysis. This target site is typically a data warehouse, a unique database designed for efficient reporting. 

The ingestion process is the backbone of an analytics architecture. It is because downstream analytics systems rely on consistent and accessible data. Collecting and cleansing the data reportedly takes 60-80% of the time in any analytics project, so plan accordingly.

Regarding data ingestion, there are two main approaches: streaming and batch. Streaming involves continuously collecting data (such as from sensors while collecting data in large batches (such as from databases). 

Both approaches have advantages and disadvantages that should be considered when choosing which best fits your needs.

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6. Data Visualization

Data visualization is the process of turning data into visual representations. When you look at a graph or a chart, you can see the information much more quickly and efficiently than if you read it in text.

For example, let's say you want to find out how many people in your neighborhood watch Game of Thrones. You could ask them all individually, but that will take forever! 

Instead, you could make a map showing where each person lives, then color-code it based on who watches GoT and who doesn't. This way, when someone asks, "Who watches GoT?" all they have to do is look at the map!

It's also useful for showing data about trends over time—like how many people watched GoT each season or how many people voted for Trump in 2016 compared with 2012.

7. Data Aggregation

The purpose of data aggregation is to produce a single, unified view of information rather than one that comes from many different places. It allows you to make better decisions based on your data, not just parts of it.

Data aggregation can be done manually or automatically, depending on how much work you want to put into it. For example, suppose all your data lives in spreadsheets on your computer, and there isn't any overlap except for names. In that case, it's probably easiest to just go through each spreadsheet and copy and paste the data into one document. 

You can automate this process using Python if you have multiple sources with overlapping information (like two spreadsheets with lists of employees).

8. Scrape Stock and Twitter Data Using Python, Kafka, and Spark

The cryptocurrency craze has given rise to a new class of investors: the amateur trader.

As the world of cryptocurrencies expands and people become more familiar with investing outside of traditional markets, they are also looking for ways to diversify their investment portfolio.

The US stock market is where many investors look for opportunities in traditional stocks—but it's not always easy to keep track of what's happening in this booming sector.

This project aimed to develop a significant data pipeline for user sentiment analysis on the US stock market. In short, this project scrapes social media to predict how people may feel about particular stocks in real time.

9. Scrape Real-Estate Properties With Python and Create a Dashboard With It

The best way to learn how to be a data engineer is by doing it. This project teaches you how to scrape HTML web pages and build Python scripts that interact with them. The goal is to create a tool that you can use to optimize your choice of house/rental property.

The project uses tools like Beautiful Soup and Scrapy to collect data from the web. Interestingly, this project covers Delta Lake and Kubernetes, currently hot topics in the data engineering world.

Lastly, no good data engineering project is complete without a clean UI showing your work. The sheer variety of tools this project uses makes it perfect for a portfolio.

10. Focus on Analytics With Stack Overflow Data

Analyzing public Stackoverflow is a great way to leverage your data skills and make a difference in the world.

There are many opportunities for you to analyze this data, from the basic to the complex.

You could analyze what questions are asked, which programming languages are used most often, and what types of comments and questions are being asked on Stackoverflow forums.

Stackoverflow provides an incredible opportunity for you to work with data in a way that will impact the world around you.

11. Scraping Inflation Data and Developing a Model With Data From CommonCrawl

The Common Crawl is a project that aims to collect the entire web, providing researchers and developers with a considerable amount of data. The data is stored in petabytes, and you can use it for various projects.

Dr. Usama Hussain conducted another exciting project using this data. He measured the inflation rate by tracking the price changes of goods and services online. 

In this project, Dr. Hussain used petabytes of webpage data contained in the Common Crawl to create and display his results. It is another excellent example of creating and displaying a data engineering project. One of the challenges is how hard it can be to show off your work—no matter how skilled you are at what you do!

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1. What are good data engineering projects?

  • Smart IoT Infrastructure
  • Aviation Data Analysis
  • Shipping and Distribution Demand Forecasting
  • Event Data Analysis 
  • Data Ingestion 
  • Data Visualization
  • Data Aggregation
  • Scrape Stock and Twitter Data Using Python, Kafka, and Spark
  • Scrape Real-Estate Properties With Python and Create a Dashboard With It
  • Focus on Analytics With Stack Overflow Data
  • Scraping Inflation Data and Developing a Model With Data From CommonCrawl

2. What is a data engineering example?

Data engineering is collecting and organizing data from many different sources and making it available to consumers in a helpful way. Data engineers must understand each system that stores data, whether it's a relational database or an Excel spreadsheet. 

They analyze that data, transform it as needed, and then store it where other systems can use it. It allows companies to take advantage of the information they have accumulated in disparate systems—such as tracking customer behavior across multiple platforms—and make better business decisions based on that information.

3. What are some examples of engineering projects?

Data Engineering Projects for Beginners:

  • Smart IoT Infrastructure
  • Aviation Data Analysis
  • Shipping and Distribution Demand Forecasting
  • Event Data Analysis 
  • Data Ingestion 
  • Data Visualization
  • Data Aggregation
  • Scrape Stock and Twitter Data Using Python, Kafka, and Spark
  • Scrape Real-Estate Properties With Python and Create a Dashboard With It
  • Focus on Analytics With Stack Overflow Data
  • Scraping Inflation Data and Developing a Model With Data From CommonCrawl

4. Which SQL is used in data engineering?

 Relational databases can be managed using Structured Query Language (SQL), a standard programming language for querying and collecting data.

5. What is ETL data engineering?

ETL, or extract, transform, and load, is a process data engineers use to access data from different sources and turn it into a usable and trusted resource.

The goal of an ETL process is to store data in one place, so end-users can access it as they need it to solve business problems.

ETL is a critical component of any data-driven organization because it helps ensure that the correct information is available in the right place at the right time.

6. What are ETL projects?

Extract, Transform, Load (ETL) is a set of procedures that includes collecting data from various sources, transforming it, and storing it in a single new data warehouse. This process can be performed by software or human operators.

ETL is used to perform data science tasks, such as data visualization. These tasks are meant to provide insights into understanding a particular business problem. It is also used for other purposes, such as reporting and monitoring.

7. How can I start data engineering?

  • Get a degree in computer science or engineering.
  • Take a Python programming course (or learn to code on your own).
  • Become an expert in SQL, Pandas, and Spark.
  • Learn about data warehousing techniques and infrastructure.
  • Get certified as a data engineer from a reputable organization.
Our Professional Certificate Program in Data Engineering is delivered via live sessions, industry projects, masterclasses, IBM hackathons, and Ask Me Anything sessions and so much more. If you wish to advance your data engineering career, enroll right away!


Are you looking to further your career in data engineering?

Do you want to master crucial data engineering skills aligned with AWS and Azure certifications?

If so, Simplilearn's Professional Certificate Program In Data Engineering is what you need. It's applied learning program will help you land a job in the industry, providing professional exposure through hands-on experience building real-world data solutions that companies worldwide can use.

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