How to Start Your Next Data Engineering Project

How to Start Your Next Data Engineering Project

Many programmers who are just starting out struggle with starting new data engineering projects. In our recent poll on YouTube, most viewers admitted that they have the most difficulty with even starting a data engineering project. The most common reasons noted in the poll were:

Let’s talk about each of these points, starting with the array of tools you have at your disposal.

For the first part of this project I am going to borrow from Thu Vu’s advice for starting a data analytics project.

Why not look at a data engineering job description on a head-hunting site and figure out what tools people are asking for? Nothing could be easier. We pulled up a job description from Smart Sheets Data Engineering. It is easy to see the exact tools and skills they are looking for and match them to the skills you can offer.

Below are several key tools that you will want to include.

One of the first things you’ll need for your data engineering project a cloud platform like Amazon Web Services (AWS). We always recommend that beginning programmers learn cloud platforms early. A lot of a data engineer’s work is not completed on-premise(anymore). We do almost all work in the cloud.

So, pick a platform you prefer: Google Cloud Platform (GCP), AWS, or Microsoft Azure. It’s not that important what platform you pick out of these three; it’s important that you pick one of these three because people understand that if you have used one, it is likely you can easily pick up another.

In addition to picking a cloud provider, you will want to pick a tool to manage your automated workflows and a tool to manage the data itself. From the job description Airflow and Snowflake were both referenced.

These are not the only choices, by any stretch of the imagination. Other popular options for orchestration are Dagster, and Prefect. We actually recommend starting with Airflow and then looking to others as you get more familiar with the processes.

Don’t be concerned with learning all these tools at first; keep it simple. Just concentrate on one or two, since this is not our primary focus.

Data sets come in every color of the rainbow. Your focus shouldn’t be to work with already processed data; your attention should center on developing a data pipeline and finding raw data sources. As the data engineer, you will need to know how to set up a data pipeline and pull the data you need from a raw source. Some great sources of raw data sets are:

Pulling data involves a lot of work, so when you are picking your data sets, you will probably find an API to help you extract the data into a comma-separated value (CSV) or parquet file to load into your data warehouse.

Now you know how to find your data sets and manipulate them. So, what do you do with all this raw data that you’ve got?

An easy way to display your data engineering work is to create dashboards with metrics. Even if you won’t be building too many dashboards in the future, you will want to create some type of final project.

Dashboards are an easy way to do so.

Here are a few tools you can look into:

With your data visualization tool selected you can now start to pick some metrics and questions you would like to track.

Maybe you would like to know how many flights occur in a single day. To build on that, you may want to know destinations by flight, time, or length and distance of travel. Discern some basic metrics and compile a graph.

Anything basic like this will help you get comfortable figuring out your question of “why?” This question needs to be answered before you begin your real project. Consider it a warm-up to get the juices flowing.

Now let’s go over a fe w project ideas that you could try out.

Beginning Data Engineering Projects Example: Consider using tools like Cloud Composer or Amazon Managed Workflows for Apache Airflow (MWAA). These tools let you circumvent setting up Airflow from scratch, which allows you more time to learn the functions of Airflow without the hassle of figuring out how to set it up. From there, use an API such as PredictIt to scrape the data and return it in eXtensible markup language (XML).

Maybe you are looking for data on massive swings in trading over a day. You could create a model where you identify certain patterns or swings over the day. If you created a Twitter bot and posted about these swings day after day, some traders would definitely see the value in your posts and data.

If you wanted to upgrade that idea, track down articles relating to that swing for discussion and post those. There is definite value in that data, and it is a pretty simple thing to do. You are just using a Cloud Composer to ingest the data and storing it in a data warehouse like BigQuery or Snowflake, creating a Twitter bot to post outputs to Twitter using something like Airflow.

It is a fun and simple project because you do not have to reinvent the wheel or invest time in setting up infrastructure.

Intermediate Example: This data engineering project is brought to us by Start Data Engineering(SDE). While they seem to reference just a basic CSV file about movie reviews, a better option might be to go to the New York Times Developers Portal and use their API to pull live movie reviews. Use SDE’s framework and customize it for your own needs.

SDE has done a superior job of breaking this project down like a recipe. They tell you exactly what tools you need and when they are going to be required for your project. They list out the prerequisites you need:

In this example, SDE shows you how to set up Apache Airflow from scratch rather than using the presets. You will also be exposed to tools such as:

There are many components offered, so when you are out there catching the attention of potential future employers, this project will help you detail the in-demand skills employers are looking for.

Advanced Example: For our advanced example, we will use mostly open-source tools. Sspaeti’s website gives a lot of inspiration for fantastic projects. For this project, they use a kitchen-sink variety of every open-source tool imaginable, such as:

In this project, you will scrape real estate data from the actual site, so it’s going to be a marriage between an API and a website. You will scrape the website for data and clean up the HTML. You will implement a change-data-capture (CDC), data science, and visualizations with supersets.

This can be a basic framework from which you can expand your idea. These are challenges to make you push and stretch yourself and your boundaries. But, if you decide to build this, just pick a few tools and try it. Your project doesn’t have to be exactly the same.

If you are struggling to begin a project, hopefully, we’ve inspired you and given you the tools and direction in which to take that first step.

Don’t let hesitation or a lack of a clear premise keep you from starting. The first step is to commit to an idea, then execute it without delay, even if it is something as simple as a Twitter bot. That Twitter bot could be the inspiration for bigger and better ideas!

Article originally posted here by Ben Rogojan. Reposted with permission.

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