Data engineering is one of the most important branches of data analytics. Do you want to become a data engineer and don’t know how to start setting up your project? You’ve come to the perfect place.
In this article, we will show you the steps you need to take to begin the ideal data engineering project. We also provided some ideas that you can work on. If you need further help, get yourself acquainted with data engineering services to find out more.
Define your goal
No matter what project you are working on, the key to its success is setting a goal. In addition, it is also important to understand the activity of which your project is part. Without it, it will be difficult for you to take the next steps. This first phase will help you choose the right tool and collect high-quality data. So the first thing you need to do is analyze the processes you want to improve with data. Then you should create a plan and define KPIs. Thanks to this, you will have the motivation and a clear goal of what you want to do with the collected data.
Get a dataset
With your goal in mind, you can move on to the second phase of your project creation – data collection.
To make your data engineering project great, you need to focus on developing and configuring your data as well as finding data from the raw source. Examples of data sources include data sets at Tableau or Kaggle, The Library of Congress Dataset Repository, OpenSpace, Open Street Map, and many others. And how to get useful data?
- Use API interfaces
- Connect to the database
- View the datasets contained in the CSV files
You already have the data you need. What’s next?
Pick tools and work with them
When choosing the tool to work on, focus only on those that you will really need. It isn’t necessary to learn how all of them work. All you have to do is choose a few of them – those that will be interesting and useful for you. So you can process data with Python. And if you prefer the data science route, you could use a Jupyter notebook and process the data. Then you can add Apache Kafka or Kinesis if you are working on AWS.
A CLOUD PLATFORM
In your data engineering project, you will definitely need a cloud platform. This is due to a lot of work being done in the cloud rather than on-premise. An example tool is Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure. Importantly, it is not necessary to select all three tools. Choose one of them so as not to complicate your work with data.
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WORKFLOW MANAGEMENT PLATFORMS AND ANALYTICAL SYSTEMS FOR DATA STORAGE
Using workflow management software, you can improve the way you work as these platforms provide flexible tools. By implementing workflow software, you can:
- Create a more effective workflow
- Automate work processes
- Eliminate redundant tasks
- Improve efficiency
Moreover, analytical systems for data storage are also crucial. The Snowflake platform provides all the tools you need for storing, retrieving, analyzing, and processing data in one place, with easy access to it.
Visualize your data
It’s time to visualize. This is one of the best ways to present a large amount of data. Dashboards with metrics are an excellent solution. You can use tools like Tableau, Microsoft Power Bi, Domo, Yellowfin, etc. As soon as you have selected your data visualization tool, you can start picking metrics to track.
3 data engineering project ideas
SET UP A DATA WAREHOUSE
Building a data warehouse is the best way to start your adventure with data engineering. Moreover, it is the most popular skill in the profession. The goal of a data warehouse is to collect data from multiple sources and transform it into a standard format. The data warehouse helps in the strategic use of data.
DATA PIPELINES: BUILD AND ORGANIZE
If you are a novice data engineer, you should start by building data pipelines. In this project, your main task is to manage data pipeline workflow using the software. For this, the Apache Airflow platform is used. For any data engineer, it’s crucial to manage data pipelines.
CREATE A DATA LAKE
Data lakes are the central repository that stores data from sources in their original format. So you can add data without having to modify it. Thanks to this, the process is fast, and data is added in real-time. Data lakes are becoming more and more important, so it’s important to know how to build them.
Now the ball is in your court
Creating your data engineering project is not an easy task. However, we hope that our post has shown you the direction in which you should go and that the example ideas inspired you to act.
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