Quality Coding for Data Engineering

Engineering is the application of math and science to solve problems. And, data engineering is the process of designing and building data pipelines to transform & transport raw data into data that is in required usable formats. The data from various sources are collected at one place like a data warehouse or data lake and then transformed into a usable form.

Over the last decade or so, companies have completed their digital transformation. This transformation has led to the continuous generation of a huge quantum of data. Companies follow different processes and standards to ensure the quality of data and coding across organizations. Some universal principles can help enhance and increase development speed, improve code maintenance and also make working with data a lot easier.

• Functional programming is excellent for working with data. Any data engineering task can be accomplished by taking input data streams and applying the required function to them. Its output data can be stored in a centralized repository, or it can be used for reporting or data science use cases. The functional programming paradigm is very common in data engineering. It can help create codes that can be used across many different data engineering tasks and can be easily tested.
• Function designing/coding is a good practice to write functions to do a single or one task. This makes it easier to identify and fix errors when a single element is to be traced. The Main function can help tie single-use functions.
• Naming conventions are very important and convey the intention and use of the code to anyone who looks at it. Using verbs as function name works. The clearer the naming of the code, the easier it is for anyone to identify the function of the code. Global variables can also be named in UPPER CASE to distinguish them from local variables. Naming a code properly makes it self-documenting and easier to understand the intention behind the code.
• After proper naming, good structure, and relevant coding make it easier to maintain and understand. The simpler the code, the easier it is to read and follow.
• Documentation or Logging is extremely important as part of good coding practices. It can be useful to document why the code is doing what it is supposed to do, then just document what the code is supposed to do. Using stings like docstrings, or any annotations to document the input and output of the function is, makes it an extremely good coding practice.
• Documenting hard coding is essential and lets a programmer or a data engineer know why any value is hardcoded in the code. With clear documentation, hard-coded values remain a mystery and should be avoided.
• Keep the code simple while creating complex code which a simple function can do, which may be considered over-engineered.
• Think long-term with modules created to be used across projects, or reused, which can take some time to develop, but this effort helps pay for itself later.
• Error prevention in case of a failure or if a job is aborted with errors, then all change should be rolled back. This will make it easier to trace out bad code and prevent further errors.
• Dependency on other systems should be considered and taken into account.
• Schema validation is an important part of coding. The schema should be defined while any input data is being read. Any data that does not match the schema should be discarded.
• Each team is responsible for the data that is loaded or is moving in and out of the system or application. The team is also responsible for backups, updates, changes, and data storage.
• The main objective of a pipeline is the speed at which the data can be processed and made available to users. Here a streaming data approach is very useful as it can identify gaps and data-related problems much faster. This helps in early rectification.
• Clean data is essential. Programs can be applied to data for cleaning it and help in detecting, dropping, and correcting the records for pipelining process.
• Automation via scheduling, and deployment is better than the manual process.
• Storage selection is important. Storage of data can be based on use or service rather than having one common storage for all sorts of data. This will also help in accidentally not exposing data to other services.
• Removal of dead or zombie code keeps the structure clean, and easily maintainable. It also helps in understanding the structure clearly.
• A data-based pipeline based in the cloud can accommodate many tools and platforms which need to communicate with each other. It takes time and effort to build connections between source systems, data warehouses, and, lakes along with analytics. It makes more economical sense to invest in tools that have built-in connectivity.

To conclude, best practices for data engineering help and are necessary to ensure high quality of data and also maintainability of code. A specialist firm offering Data Engineering services can help you organize and convert all the data into usable formats to design, manage and optimize the flow of data.