10 data engineering best practices to follow in your company

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The importance of a rigorous data engineering process

In an increasingly data-driven world, an effective data engineering strategy is key to maintaining efficiency within your data management and reporting processes. As a key factor in making informed business decisions, your data engineering process should follow best practices to ensure your data is accurate and of the highest quality.

How do you maintain best practices in data engineering?

Often the simplest steps are the most effective in preventing future difficulties. We’ve compiled the best practices for data engineering to ensure your company manages its data effectively. 

10 data engineering best practices

Keep your code simple

Reading and analyzing code can be a time-consuming process. By removing the unnecessary elements and ensuring your code is readable, it will be easier to follow and require less time to process. To keep your code easy to maintain, remove dead portions of code, code duplications and those that have little functional use. This isn’t just beneficial for your data engineers, but also ensures smoother cooperation with your other specialist teams when handling datasets.

Leverage functional programming

Functional programming is a key asset for data engineering. Applying some function to input data and serving it for reporting or data science use aids most data engineering tasks and makes large volumes of data more manageable. A functional programming paradigm enables the creation of reusable data and a code that can be used across multiple data engineering tasks.

Create clear documentation

Documenting what your code is doing and why streamlines data processing across teams. By providing proper explanatory descriptions of pipelines and components, your team can collaborate effectively, even if the actual owner is not available to modify the data themself. Your documentation should focus on explaining the intent of the code for anyone encountering it for the first time.

Maintain a high level of data quality

Performing regular data validity checks should be a priority for your organization. Taking the time to perform data cleaning and data validation processes allows you to remove invalid data and make any necessary amends, helping to keep your code optimized in the long run. Select an open-source or commercial data cleaning tool that accommodates your requirements and apply it to your datasets before applying it to your machine learning models or business insights.

Take a modular approach

Taking the time to build a data process using small, modular steps enables valuable benefits further down the line. By breaking data processing down into clearly outlined steps with a clear focus on improving an aspect of code, each module can also be independently adapted at each stage of your project growth. This in turn ensures your process remains adaptable to your changing needs.

Ensure you’re using the right tool for data wrangling

Designed to detect and correct entities within the data engineering pipeline, a data wrangling tool can help to keep large quantities of data clean and organized by tackling any inconsistencies within a data set. This prevents problems with the loading and analysis of data further down the line, helping your data engineers to provide more accurate insights.

Invest in tools that offer built-in connectivity

Modern cloud-based platforms are designed to accommodate communication and collaboration across multiple users, tools and other platforms. Investing in tools that have built-in connections to each other saves the time and manpower needed to build connections.

Implement a data security policy

It should be clear how the data you collect and process is used, who uses it and where it’s shared to prevent any potential regulatory issues. A comprehensive GDPR policy should classify data sensitivity, monitor access to sensitive data, develop a data usage policy and make use of endpoint security systems for multi-factor authentication, policy documentation and protection.

Streamline pipeline development processes

Pipelines should be built in a test environment, such as a cloud data platform, where the effectiveness of code and algorithms can be measured extensively before they are applied to a production environment. With a cloud data platform as the foundation for running data pipelines, you can easily clone an existing environment for a new test without losing the databases and infrastructure. Building pipelines that can be easily modified allows you to continually integrate and deploy within the data pipeline as you develop and grow at scale.

Plan for the long-term

Your data engineering solutions should reflect the goals of your organization, a key one of which will be growth. Use your data engineering processes to identify your potential challenges in advance and prepare for them, and to also leverage growth opportunities. The premise underlying every data engineering solution should be to continually strive for improvement for long-term gain, so regularly monitor and evaluate your processes, tools, teams and results. By creating solutions that will make your operations easier in the long term, such as data pipelines, you put your data processes in good stead for a later payoff.

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