Implement data engineering best practices

by Ana Lopez

Einat Orr is the co-founder and CEO of Treeverse, the team behind moreFS. She is a technical leader and has a PhD in mathematics.

It may sound counterintuitive, but an economic downturn can be the best time to implement data engineering best practices. Investing in an initiative like this may be the last thing on your mind right now, but as customer acquisition teams face challenges in the coming quarters, retaining existing customers will become a priority for many.

Organizations that provide high-quality data to consumers and base their decisions on it can reduce customer churn and cut costs to help them navigate the current and upcoming slump. In addition, they will be better prepared for the future bull market, which, as history shows, inevitably follows any recession.

Overall, I believe that companies that implement engineering best practices can avoid costly mistakes, get their teams working faster, and achieve their goals faster. This is relevant for almost every organization that deals with big data.

Investing in data engineering

By applying proven data engineering processes and tools, your employees can increase their effectiveness and produce better products in shorter time frames. It also opens the door to talent retention, as most data engineers prefer to work with modern tooling. Your bottom line will also benefit from a reduction in data storage costs, faster error elimination, process automation and increased engineering productivity. Other areas of focus for best practices include the following.

Data quality

By improving your data engineering efforts, the insights from data are of higher quality. If decision makers use the data, it helps them steer the boat in the right direction. If your customers are on the receiving end of the data, you can retain them more easily due to the higher quality of your product.

Data rate

Once you implement best practices and reduce the cost of errors, your business can run much faster. The data can also provide new insights and lead to new data features or product development which in turn create opportunities to upsell existing customers or bring in new customers who need these new features.

Be ready for departure

Incorporating data engineering best practices can help you proactively address potential issues as you scale your product to serve a larger customer base and expand your R&D team to meet demand for new features. to fulfil. This ensures a smoother process and minimizes disruptions as your business grows. You will reap value by not only building a valuable product, but also growing sustainably.

Data engineering best practices in times of economic downturn

Improve team collaboration

Data engineering teams that develop, test, ship and maintain complex data products usually account for at least several people on your team. But they’re not the only ones consuming and modifying data; those of other departments do the same.

This requires data practitioners to collaborate and at the same time be able to work independently. For this to work, organizations must use tools that enable secure development in an isolated environment with the ability to continuously merge individual work.

Increasing data quality

Give your team the right tools to identify and remediate data errors that can be costly. Rollback can give your team time to diagnose the root cause of issues and develop a solution.

Teams can go one step further and detect errors before they happen using prevention tools. This helps the organization retain customers during the recession by providing high-quality features.

Giving your team the right tooling

Implementing data engineering best practices is only possible with modern tooling. To work faster, data teams need tools for the following.

• Version management of data. This is a version control tool that helps manage changes in datasets and models by providing a means to record and track changes in a particular dataset/model.

• Orchestration. This is essential for organizations with multiple data systems, providing access to the data teams need in a format they use exactly when they need it.

• Quality testing. Testing should be performed at every stage of the data lifecycle.

I’ve seen firsthand how the open source community is a great resource for building tools that incorporate data engineering best practices. This community can help you unlock the value of these various tools without having to spend money on purchasing or deploying them.

Three elements of a successful implementation

1. Tools. Once you know which practices you want to implement, choose the right tools for the job.

2. Process. With tooling, you can start implementing the processes and add barriers or tracking points within these tools to ensure that people act in accordance with your designed practices.

3 people. At the end of the process, you will see a change in culture prioritizing learning and improvement and openness to new approaches and tools.

Prepare for the future from a data perspective

Many organizations are now reviewing their processes and tooling to optimize and streamline their processes. If you decide to integrate data engineering best practices, you can help your team work faster, while potentially reducing costs and increasing productivity.

Organizations that don’t implement these best practices now could find themselves in trouble and unable to benefit as effectively from the changed market conditions once the crisis is over.


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