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Ensuring the high efficiency of data processing is not easy. Do you have a feeling that you could improve your day-to-day work? We’ll provide you with 10 data engineering best practices you can follow in your business in order to work smarter, instead of harder.

Nowadays, companies operate in a data-driven world. Every day, organizations collect innumerable amounts of information that can be used to improve their effectiveness. That leaves data engineers and analysts with loads of work. Data engineering is not an easy job. It is made even more difficult by the fact that it is one of the fastest evolving professions, which means that data engineers have to educate themselves all the time. There are many ways to ensure data and code quality, and professionals need to know them well in order to select the best method for the organization they work in. Check out our 10 data engineering best practices. 

1. Assess your data stack regularly

IT companies introduce new features and solutions all the time. As a specialist, you surely know about this, but we just want to remind you to update your software regularly and upgrade to the newer versions of your tools when they become available. You would be wise to subscribe to newsletters or follow the social media profiles of your tech stack providers. This way, you will stay informed about the newest features and products that may improve the efficiency of your work. Working with a modern data stack is essential for your success.

2. Control processing efficiency

Another quite obvious best practice for data engineers is to monitor the efficiency of your processes. A well informed data engineer should know how long it takes to process a certain amount of data. With this knowledge about the optimal processing speed, you can immediately spot when a process slows down, track down the cause and react accordingly. Monitoring your systems and process efficiency provides you with a great deal of information about the maturity of the process, compliance, and system (and source) integration. Moreover, by searching for errors, you can deal with them faster, before they result in serious delays.

3. Leverage functional programming

Python is one of the most often used programming languages in the world of data engineering. Many popular tools are based on it (for example, Airflow, which we use in our projects). Python allows its users to combine object-oriented and functional programming in their work. You can carry out almost any data engineering task with functional programming. All you need to do is take the input data, apply some appropriate function, and then you can load the output to the centralized repository or use it for reporting or data science. Functional programming allows data engineers to develop code that can be easily tested and reused in many data engineering tasks.

4. Keep your code simple

And – since we’ve mentioned coding – keep your code simple. Data engineers spend a lot of time reading and analyzing their code – it probably occupies them much more than actually writing the code. By making it easy to follow and readable, you can save yourself a lot of struggling later. By following data engineering best practices when writing code, you will simplify your future work and ensure smooth cooperation with other specialists who work with it or join the team. 

Simple code means “concise” code. The less you write, the less you have to maintain. Also, you have to remember to remove dead, abandoned portions of code. Don’t be afraid of evaluating and improving your code, even if that means that you have to remove some of its useless parts.

5. Stick to the design patterns

It is easier to keep order in your processes when you have some predefined rules and design patterns that every member of your team knows and follows. Creating data patterns and an overall strategy for working with data will help you work efficiently, and it will reduce your errors and challenges. Plan ahead to use certain tools, frameworks, processes and techniques when dealing with data in your organization. 

You can put your faith in patterns designed by somebody else if they fit your use cases. If not, try another, adapt one to make it suit your purposes or come up with your own (just remember to test it before implementation). Having established design patterns will keep your team on track and significantly improve communication on the project.

6. Ensure data quality

Do you imagine training your machine learning models on datasets that consist of duplicated, incomplete or inaccurate data? Of course not. No matter if you use data for business intelligence or for artificial intelligence use cases – if you don’t perform data validity checks, you can’t truly trust your work results.

Carefully plan your data validation and data cleaning processes. Get rid of invalid data and fix what can be useful for your project. Pick the best open-source or commercial tools for data cleaning and apply them to datasets before you use the information you collect to produce business insights or train machine learning models. 

7. Leverage process automation

Respect your own time. Leveraging process automation is a data engineering best practice for two reasons. First, thanks to it, data engineers don’t have to waste additional time performing manual tasks – it is all done automatically, based on pre-defined rules. Second, the degree of human error is reduced.

8. Create clear documentation

Having clear documentation is a crucial matter in any business. Without proper documentation, it would be really difficult and time-consuming to handle onboarding of new team members, cooperate with other parties or move a project to another team. Good project documentation should be detailed, but concise at the same time. It has to be written in simple language, so anybody can understand it. Avoid using rare and unnecessary technical terms if they won’t be useful for future readers.

9. Organize your team collaboration

It may be difficult to control and manage what is going on in a project if you forget about some data engineering good practices related to cooperation. First, you should assign roles to your users and, based on them, grant users necessary permissions to use your systems and tools. It is a good idea to enable logging. This way, users will be provided with information on who worked on a certain job and what they did. 

Make collaboration easier by encouraging proper naming for pipelines and expecting users to add descriptions to pipelines, jobs, processors, executors and other elements whenever possible. With descriptions, you will be able to quickly learn why some components have been created by other team members. This not only improves collaboration but also simplifies project maintenance.

10. Always think long-term

The main goal of companies is to grow – that is why you should not think small. Instead, think ahead. Try to predict your potential challenges and opportunities for growth, and come up with the tools and processes you may need in the future. Focus on solutions that can be reused across various use cases. All the time, you have to monitor, evaluate and improve – that goes for both your and your team members’ skills, processes, and tools. 

Have you encountered some challenge you can’t deal with on your own? Contact us, and tell us more about it. We will be happy to help.

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Author

  • Paweł works with cloud computing technologies, especially as a Data Engineer on the Google Cloud Platform. His favourite areas are data pipeline performance tuning and financial optimization. He loves mountain sports and exciting cuisine.

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