7 Data Engineering Best Practices to Follow

Category

Blog

Author

Wissen Team

Date

May 28, 2024

Although most organizations today are driving efforts toward gathering data from various sources, making quality data available is increasingly becoming a challenge. Since quality data is a major determinant of the success of most data analytics initiatives, data engineers need to be at the top of dependencies and requirements as they design and build their data pipelines.

Read on as we throw light on the top data engineering best practices to follow for making informed decisions.

Top Data Engineering Best Practices

Data engineering is quickly becoming a top priority for businesses of all sizes as it helps them get answers to some of the most burning questions about their business, customers, market, competitors, regulatory environment, and more.

Right from how much it costs to acquire a new customer, what changes to make to the product line to stay relevant, or what steps to take to comply with new compliance standards – as data volumes grow, data engineering helps in designing and building modern systems. These systems assist in collecting, storing, and analyzing data at scale.

As more and more businesses require data to be at their fingertips for quick and accurate decision-making, data engineering helps maintain and improve the organization's data infrastructure. Today, data engineering has become one of the fastest-growing professions. It's noteworthy, however, that effective outcomes result from careful planning and adopting best practices that enhance the quality and speed of data analysis and decision-making.

  1. Use the Right Tools

One of the most important aspects of data engineering is the adoption of the right tools. Before selecting a tool, make sure to deep dive into all its capabilities and verify if they align with the data engineering goals of your business. Make sure to do sufficient homework on different tools, so they can become extensions of your data analytics team – and not hurdles in your data journey.

The right tool can help build, monitor, and refine complex data models and improve business outcomes by harnessing the true power of data.

  1. Focus on the Three Rs

Repeatability, replicability, and reproducibility are the three pillars of any data engineering project. Repeatability ensures the same team uses the same experimental setup on different trials to produce the exact same result.

Replicability ensures different teams use the same experimental setup on different trails. And reproducibility ensures different teams use different experimental setups for testing data. These three aspects help measure the quality of data experiments, backed by independent verification, and verify that findings are correct and transparent.

  1. Ensure Modularity and Scalability

Another best practice to follow in the data engineering project is to ensure modularity. Building data processes in small, modular steps helps solve specific problems, making data analysis more readable and easier to test.

At the same time, embracing technologies like cloud and automation can help build pipelines that can easily be modified and scaled – based on the current business requirements.  

  1. Enable Continuous Monitoring

Building continuous monitoring and alerting into the data pipelines is a great way to ensure everything is working as expected. Since you can't fix what you don't know is broken, such monitoring can throw much-needed light on poor quality data, bad records, missing data, etc., and allow for catching failures and taking corrective action.  

  1. Build for Failure

No matter how reliable the data pipeline might seem, it is always a good practice to assume failure – and plan accordingly. Any data engineering project will always be in a state of flux, especially for those that keep growing.

With so many components moving in and out of the data pipeline, it is important to be aware of potential points of failure, associated consequences, and remediation steps you can take to make sure everything keeps functioning – without causing any interruption in data quality or user experience.

  1. Ensure Your Pipeline Handles Concurrent Workloads

As businesses need to run multiple data engineering projects simultaneously, it is important for each project to keep up with the demand. With data coming in 24 hours a day, seven days a week, and from multiple sources, the data pipeline should be able to collect, store, and process this data continuously – even as data engineers are analyzing the data and applications are processing it for further use.

Using cloud-based data, pipelines support shared data architectures and multi-cluster environments, allowing teams to allocate multiple independent, isolated clusters for data loading, transformation, and analytics.

  1. Set Appropriate Security Policies

While working on any data engineering project, it is also critical to have appropriate security policies and access control measures in place. These policies help determine which users can view data and what level of access they have to it, thus thwarting security or regulatory issues.

By setting the right security policies, businesses can:

  • Monitor access to sensitive data
  • Develop necessary data usage policies, and
  • Ensure the data is suitably encrypted before it is distributed

This will allow them to harness the power of data to drive profitability and growth for years to come.

Conclusion

Data engineering, although a challenging field, is also one that pays huge dividends. Considering that data volume is expected to grow to more than 180 zettabytes by 2025, following these well-established best practices can help you avoid unnecessary expenses and build a reliable and repeatable data pipeline aligned with your business goals.