Ensure Code And Data Quality: Adopt Good Data Engineering Practices

The success of data analytics depends on the availability of reliable quality data. This is irrespective of the tactics of data analytics used. This is where data engineering comes into play.

What is data engineering?

We know that engineers are tasked with designing and building things. A data centre engineer thus deals with designing and building data pipelines. These help in the transformation of data into a specified, highly usable format and then transported to data scientists or end-users. Collection of data happens from several disparate sources. They are then stored in a virtual warehouse to ensure data uniformity and quality. Data engineering is important as it helps:

· To increase development speed,

· Enhance code maintenance and

· Make it easy to work with the data.

Useful data engineering practices

Different data engineering practices help to achieve this like:

· Functional programming: This is one of the more popular paradigms of data engineering. It lets us create codes for reuse in several data engineering tasks. Testing of this code involves feeding small units of data into the function and running ETL on the production data. Almost all data engineering task can be easily achieved by:

o Taking data inputs,

o Performing some function on it and

o Loading the generated output into either

§ A centralised repository,

§ Serving it for reporting or

§ Using it in data science.

· Designing functions to do one single thing: Writing functions to do one thing ensures their reusability. A good data systems engineer always follows this practice. You can always build a main function that ties together all the different pieces. Additionally smaller functions help in faster code development since the identification of the failing unit becomes easy. Single components enable easy to exchange for use in different combinations and permutations in different use cases.

· Naming conventions to be properly followed: Object naming facilitates easy recognition of the same. Abbreviations may not be always understandable and hence writing full names makes for good data engineering practice. Some conventions that are commonly followed in data engineering include:

o Using verbs as function names,

o Longer names are better understood,

o Using upper care for global variables,

o Defining imports on the top of scripts.

Ideally, naming makes a code self-documenting and helps in writing codes faster.

· Writing less but better codes: This is a favourite rule of every python data engineer. Generally, we read codes with greater frequency than we write it. If codes are readable, they are easy to follow. Thus, using a good structure and proper naming while writing codes, make them easy to understand. Concisely written codes are also easier to maintain.

· Proper documentation is the key: Instead of documenting what a code does we need to document why it does it. Talking about what a code does is like stating the obvious. However, mentioning why a code does what it is doing provides the necessary information to work on the code. You also become a better engineer if you use type annotations and docstrings to document function input and output.

· Avoid retaining zombie codes: These abandoned, commented out codes left in the script have no function. They were probably written to test a new behaviour or retained as a backup etc. Since they confuse later engineers and it is best to leave them out.

· Business logic should be separate from utility functions: While mixing the two makes sense, separating them makes better practical logic. This separation requires extra upfront effort but pays off in the long run as it enables reusability and has the benefit of defining a single functionality once only.

· Keeping it simple: Most data engineering services tend to create complex and sophisticated solutions. If a simple function works by taking input data and providing output in a transformed form, there is no need for a custom class object here. It makes for a perfect example of over-engineering and needs avoiding.

· Long-term thinking: Creating solutions for reuse across several disparate use cases makes life easy for the engineer in the long term. However, they take longer to develop. However, it is best to opt for them as this extra effort helps save time and effort later on.

The aim is to ensure the superlative quality of data and easy maintainability of codes. Using the above best practices make like easier for the data engineer and ensures this goal is met.