Why did the data engineer get arrested at customs? Because she was caught importing pandas.
Now that the joke’s out of the way, it’s time to talk about the different data engineering projects you might work on as you enter the exciting world of data. You can add these projects to your portfolio and show the best ones to future employers. Remember, the world’s most successful engineers all started where you are now.
All data engineering projects involve creating a data infrastructure capable of data ingestion, data storage, data visualization or something else data-related. Completing these projects will hone your data engineering skills, especially if you work with world-class instructors like those at The Data Incubator.
What to Remember When Creating a Data Engineering Project
Seeing as you’re still relatively new to data engineering, it doesn’t matter if you make mistakes when completing a project at this stage. The purpose of undertaking a project during your training is to experiment with different tools—Spark or MapReduce, perhaps?—and play around with different data structures. Once you become a more proficient data engineer and get a job, you’ll be involved in real-world projects that could make or break the success of an organization.
That said, there are a few ground rules that you need to keep in mind when creating a data engineering project:
- Before you start a new project, determine its requirements. Which data sources will you use? Which data types? What tools? What do you want your project to achieve? Write down answers to these questions somewhere and refer to them throughout the completion of your project.
- Know the limitations of your project. Remember, you’re just starting out and definitely won’t have access to huge data sets like a multinational corporation does. Nor will you have expensive tools to process, manipulate, model, or analyze data. But that’s okay! You can create a data project with data that’s already in the public domain, such as government statistics, and use open-source tools that cost you nothing. Almost anyone can create a data engineering project with resources available online.
- Get feedback. You might think you’ve created this amazing data pipeline that moves data from one location to another, but you could be mistaken. Professional advice from instructors with years of data engineering experience will help you fine-tune your projects and learn from any mistakes you make. For example, The Data Incubator lets you create data projects with hands-on training!
Examples of Data Engineering Projects You Can Work On Right Now
Sure, you might not have all the tools and technologies you need to create a data engineering project just the way you want. But don’t let that stop you! Here are some (relatively simple) examples of projects you can attempt based on your existing skills or with the help of experienced data engineering professionals on a training program:
Move Data to a Data Warehouse
This project involves coding big data pipelines that move data from a data source, say a relational database, to a data warehouse—a centralized target repository for data analysis. One of the easiest ways to do this is to use ETL, which stands for Extract, Transform and Load:
- Extract data from your data source and place it inside a staging area
- Transform that data into the most appropriate format for data analysis
- Load the data into a warehouse such as Snowflake or Amazon Redshift
Tip: You can start by using ETL tools that automate the above process and then try to create data pipelines from scratch using code. Good luck!
Move Data to a Data Lake
Once you’ve mastered ETL, why not move data from a source to a data lake using Extract, Load, Transform (ELT)? A data lake is a central repository for storing structured and unstructured data at scale. ELT pipelines are the best way to get data to this repository because you can use it to:
- Extract data from a data source
- Load it into a data lake like Microsoft Azure Data Lake
- Transform that data into a more suitable format
Again, you can use ELT tools for this process before coding data pipelines yourself.
Manually Create an API
An application programming interface (API) allows two or more software components to exchange information. You can create an API of your own by determining its protocols and definitions, either by using code or an API management tool. After designing an API, you’ll need to develop it, test it and then publish it.
Embarking on a new data project might seem daunting, but doing so will give you more confidence in your data engineering abilities and help you develop a portfolio to show future employers. For even more successful results, enroll in a data engineering program to get feedback from instructors and receive hands-on training.
What Are You Waiting For?
Ready to kick-start your data science career? There’s never been a better time than now. The Data Incubator has you covered with its data science boot camps and programs, helping you master the skills for your dream job.
You can learn more about our programs here:
- Data Science Bootcamp: This provides you with an immersive, hands-on experience. It helps you master in-demand skills to start your career in data science.
- Data Engineering Bootcamp: This program teaches you the skills to build data infrastructures, design better models and effortlessly maintain data.
We’re always here to guide you through your journey in data science. If you have any questions about the application process, consider contacting our admissions team.