10 Data Engineering Best Practices for Your Business

10 Data Engineering Best Practices for Your Business

Data is key when it comes to making informed decisions for businesses. It helps businesses get the answers to some of the most burning questions about their customers, how much it costs to acquire a new customer, or how to improve their products or services. Furthermore, the amount of data produced today is growing exponentially. In fact, 90% of data existing today has been created over the last two years.

As businesses tend to rely more on the data they have at their fingertips, the role of data engineering is becoming quite prominent.

In this article, we will go over data engineering best practices that are worth considering today. But let’s start from the top.

What is data engineering and some of its main components

Data engineering is the process of making sense of large amounts of data. It collects raw data from various sources and transforms it to make it accessible and usable to data scientists and other end users within the organization.

 Hybrid architecture for common DWH solution

Without this structuring, the large amounts of data that companies have on their hands are essentially useless as they can’t be used to drive any conclusions or affect any decisions. Data engineering provides valuable insights into available data that can make a substantial impact on a company’s growth, predict future trends, or understand network interactions.

Data engineering considers the end-to-end process as data pipelines that transform and transport data to present it in a form that can be analyzed and used to drive some insights. The pipelines take data from one or various sources and collect them in a single warehouse to consolidate one single source of truth.

Typical ETL pipeline within Airflow

The common elements of the data pipeline are:

  • Source(s) – one or various sources that data comes from, such as database management systems (e.g., MySQL), CRMs (e.g., Salesforce, HubSpot), ERPs, some SM management tools, or even IoT devices.
  • Processing steps – this involves extracting data from the sources, transforming and manipulating it following business needs, and then depositing it at its destination
  • Destination – is typically a data warehouse or data lake, a place where data arrives after being processed

Building a data-first company starts with organizing the data you have and its various sources. Data engineers here play a strategic role, having the capability to harness the full potential of data and how it affects the entire organization. When it comes to making the most of your data, there are some data engineering best practices to follow:

data engineering components

10 data engineering best practices

Make use of functional programming

Functional programming is a perfect choice when working with data. ETL (Extract, Transform, Load) is known to be quite challenging, often time-consuming, and on top of that hard to operate, advance, and troubleshoot. Applying a functional programming paradigm brings a lot of clarity to the process which when it comes to large volumes of data is essential. Additionally, it enables creating a code that can be reused across multiple data engineering tasks.

Practice modularity

Building a data processing flow in small, modular steps is another best practice in data quality and quality engineering. Modularity means that each step of the process is focused on a specific problem which makes code easier to read, reuse and test. Modules can also be easily adapted independently which comes especially in handy as the project grows. Modules that are built with a set of inputs and outputs that are suitable for numerous contexts will make data pipelines clean and also easy to understand from the outside and thus they can be easily reused in the future.

Follow proper naming convention and proper documentation

To help a team be on the same page and collaborate more effectively, it’s a good data engineering principle to practice proper naming conventions and documentation. This is especially useful when the actual owner is not available to make changes or modifications. Make it a rule inside the team to provide proper explanatory descriptions of pipelines, jobs, and components as well as provide use cases it might solve.

When it comes to naming, strive to name the objects in a way that makes it clear to a new person that might join the team and avoid confusing abbreviations. As for creating useful documentation, it should focus on explaining the intent behind what the code is doing rather than stating the obvious.

Select the right tool for data wrangling

With the large amounts of data and data sources that keep growing, it’s extremely important to keep the data clean and organized for easy access and analysis. A data wrangling tool can tackle any inconsistencies in data and transform distinct entities, for instance, fields, rows, or data values within a data set and make them easier to use. The clean is the data you feed, the data and more accurate insights you can expect. Data wrangling tools can help detect, drop and correct records prepared for the data engineering pipeline.

Strive for easy to maintain code

Being clear and concise are the principles that also apply when it comes to writing code. Making it readable and easy to follow is a good practice that will help everyone on the team to work with it in the future. Some of the best code development principles to follow here are:

  • DRY (Don’t repeat yourself) aims at reducing the repetition of software patterns and code duplications by replacing them with abstractions to avoid redundancy.
  • KISS (keep it simple. stupid) strives to keep the code simple and clean and make it easy to understand. The principle suggests keeping the methods small, never more than 40-50 lines. While each method should only solve one problem. If there are a lot of conditions in the method, it should be broken down into smaller methods. Thus, they will be easier to read, maintain, and potentially debug faster.

Use common data design patterns

Data design patterns are repeatable solutions to common, occurring problems in software design. They provide a problem-solving template that could be used as a basis for designing a solution. Creating data design patterns provides you with techniques, tools, and processes that could be used to speed up the development process. Patterns can help keep track of the existing types and counts of data pipelines as well as simplify the communication between the developers by using well-known and understood names.

Build scalable data pipeline architecture

Useful insights and analytics rely on efficient data pipelines. The ability to scale as the amounts of data sources increase is detrimental here. That’s why it’s a good practice to build pipelines that can be easily modified and scaled. This practice is referred to as DevOps for data or “DataOps” and focuses on delivering value faster by using automation and sometimes AI to build continuous integration, delivery, and deployment in the data pipeline. Embracing DataOps will improve the usability and value of data, as well as make it more reliable and easier to access.

Ensure reliability of your data pipeline

To get notified when your data pipeline fails, make sure monitoring and alerting are built-in. Focusing on the reliability of your data engineering pipeline by regularly checking on error notifications, ensures consistency and proactive security. This way the quality of data can be easily identified and monitored.

Follow some general coding principles

Some general best coding practices can also be applied to data engineering, such as avoiding hard code or dead code. To be able to utilize the code base in different environments in the future, avoid hard coding values. Instead, make your pipelines configurable. Another good practice is avoiding keeping someone’s abandoned code. Removing it will help to keep the code base clean and easy to understand for other developers in the future.

Set security policy for data

To prevent any potential security or regulatory issues, data owners or producers need to recognize and set data sensitivity and accessibility. It should be clear how the data is used, who is using it, as well as where it’s shared. Some of the steps for setting the security policy for your data include: classifying data sensitivity, developing a data usage policy, monitoring access to sensitive data, physical security of data, using endpoint security systems for protection, policy documentation, employee training, and multi-factor authentication.

Data engineering impacted by technologies such as cloud computing, IoT, and artificial intelligence, is unfolding at unprecedented speed. The decisions made regarding your data pipeline can make a world of a difference between profitability, growth, and losses. Taking into account data engineering best practices can help you avoid increased expenses, as well as time spent on unnecessary tasks.
If you are interested in building a reliable data pipeline, contact us, and our data engineers will get back to you regarding any questions, as well as help build a pipeline that is aligned with your business goals.