How to Improve Workflow in a Data Engineering Project
In today's competitive environment, companies need accurate and trustworthy data to drive innovation. But with so many tasks to deal with, data analysts can often make errors and misunderstands that can reduce their productivity. How can you avoid chaos in a dynamically developing company? The antidote to chaos is workflow, i.e., a workflow management system.
So, let's check how you can improve the workflow in a data engineering project. Find out more about data engineering services.
How to build a data engineering project
Let's briefly recall what building a data engineering project looks like. The process of creating a data engineering project consists of 5 stages.
Pick the data source
Prepare data
Analyze data
Visualize
Find the right tools
Workflow – what is it?
Workflow can be briefly defined as a look at the organization of work. Thus, it is a list of the sequence of events needed to complete the task. A workflow stage describes a business process by organizing resources and specifying the path a task must follow.
There are two well-known workflow frameworks:
CRISP-DM
CRISP-DM is a process model that provides an overview of the data mining process life cycle. The life cycle model consists of six phases, i. e. business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Each of these 6 phases has its own defined tasks. CRISP-DM is flexible and easy to customize.
OSEMN
It is a framework that consists of 5 phases for the data engineering project: Obtain, Scrub, Explore, Model, and Interpret.
5 ways how to improve a workflow in a data engineering project
1. A CLEAR DEFINITION OF A PROBLEM
No matter what project you're working on, it's important to define the problem. Often it is also challenging because you must consider many factors to diagnose the problem. You're probably wondering how this is supposed to improve the workflow in a data engineering project. The answer is very simple. Having a clearly defined problem, all team members know exactly what to focus on. Without it, the team could spend their time doing a million different things, which ultimately wouldn't add much business value.
2. DATA SOURCE MINIMALIZATION
Data has a huge impact on your workflow. Poor quality data can ruin the best data engineering project, wasting team members' resources and time. Companies use more than a dozen internal and external data sources to produce analytics and ML models. All of these sources are susceptible to unexpected changes without notice, damaging the company's decision-making data. And the data should provide leaders with valuable information so they can use it to make good decisions and improve their day-to-day operations.
3. AUTOMATED DATA TESTING
Continuous integration (CI) and Continuous Delivery (CD) are a set of rules, guidelines, work culture, and a collection of good practices for working on IT projects. With them, the development team has the opportunity to deliver certain, tested, and proven changes to the code more often. The implementation of these practices is considered one of the best and most effective ways to work on IT projects and their development.
4. GOOD ANALYTICAL TEAM
A good, healthy, and educated team of programmers is of great importance for any organization that is based on new technologies. Good data engineers are people who are helpful, creative, ambitious, and willing to share their knowledge. They have one goal, and they work towards it together. However, it takes work to create such a team. Companies often tend to hire more data analysts, engineers, and scientists. And you know, the more people in the team, the more ideas. And perhaps this is a plus, but it also comes with misunderstandings. For example, one team adding a new field to a table may cause the other team's pipeline to crash. The result may be missing or only partial data.
How can you avoid this scenario? You can build a healthy good team by using data to help you assess team health.
5. A PROPER WORKFLOW MANAGEMENT PLATFORM
Another way to improve your data engineering project workflow is to choose a tool. Among the many platforms available, sometimes it's hard to choose the right one. The best solution is to focus on a few, not a dozen or so platforms.
So, if you want to improve workflow in a data engineering project, you will need the right platform to manage it. The most popular are Airflow and Snowflake. Dagster, as well as Prefect, are also worth mentioning here. However, don't focus on all four. Start working with one platform and then look to the next one.
Conclusion
Creating a data engineering project can be a challenging task - you will surely encounter many problems. In today's article, we focused on how to improve your workflow. We have listed five ways to achieve this. With a clear goal, good-quality data, a healthy team, and the right tool, you can improve the workflow of a data engineering project.