5 Best Practices for Data Engineers to Build Data Models

The ability to build efficient data models is a key part of any data engineer’s job. Developers need to rely on a solid DataOps platform to execute the data pipelines and logic that underpin these data models. However, making a mistake while designing a new data model can have an enormous impact on the outcome, leading to incomplete, or even worse, bad insights. In addition, poorly implemented data models can be time-consuming, difficult and expensive to change.

Here are some best practices that can help data engineers ensure their data models are adequately managed, and that they are able to deliver on their vision to automate and orchestrate the data management process within their organizations.

1. Create a single source of truth. Bring the raw data from all your different sources into your data warehouse or database. You simply can’t rely on pulling “ad-hoc” data from the source itself, which can affect the entire flow of your data model. By using all the available raw data, which is hosted in your centralized hub, you will have all the historical data. Applying logic upon data that is pulled directly from a source and making calculations from it can affect and even ruin your entire model. It is also very hard to restore or maintain where something is wrong throughout the process. So, make sure you begin by having a single, comprehensive source of truth for all your raw data, from across all your sources.

2. Define the relationships between your data sources. Take all the data from all relevant sources and create the relationships, logics and connectivity between them. Establishing the relationships between your different data sources in a way in which the right logics and interdependencies are defined is key. This will enable you to create the necessary tables to produce a summarized row of all the metrics you need in one place.

3. Separate logics into unique metrics and nonunique metrics. Beware of the danger of distorting metrics in your data model as a result of not differentiating logics from unique metrics. For example, metrics such as “unique visits,” which shouldn’t be incremental, should be treated as such. Other nonunique metrics such as impressions or time on-site should be aggregated to assess their performance. Not separating them and making a distinction could really mess up your insights.

4. Monitor your data pipelines frequently. Keep an eye on your data pipelines. Monitoring is key, and ideally this should be done in real time, so you’re alerted when something is wrong. As an interconnected ecosystem, one small change can affect the rest of your data model. This is the reason why the industry is heavily investing and placing increased focus on data lineage, data quality and data governance as a whole. Managing the health of your data models and data pipelines is crucial to guarantee reliable and accurate insights.

5. Create independent processes. Creating processes within your data models that don’t rely on others is key. While all aspects of the data model should be synchronized, it is important that the processes running in parallel are also operating independently. The benefit of doing so is having the ability to separate or isolate the different parts of a model without affecting the rest. It might be about fixing a local problem or going back into historical data without having to deconstruct the entire data model.

I hope you find this advice and recommendations on best practices helpful. Data models are definitely not one-size-fits-all projects – they include the good, the bad and the ugly. Ultimately, in order to succeed, it’s critical that businesses can fully harness the power of their data with self-serviced data pipelines and flawless data models that take away risks, complexities and headaches.