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5 Best Practices for Data Pipelines

Automated data pipelines offer many advantages but the shift from manual processes can be complex. Here are 5 best practices to help.

As businesses become more data-orientated, one of the most common issues run into is the constant rise in the volume and number of sources of data, which is often not properly treated and leads to siloed data. To prevent this build-up, businesses are turning to data pipelines as a way to transfer and transform data from multiple sources to the correct end point. 

Data pipelines enable businesses to use their data in more meaningful ways, by having the movement of data be processed automatically. However, it is not a straightforward shift from manual or ETL processes to data pipelines, and there are practices and outlooks businesses need to be aware of to succeed. Here are five best practices to consider: 

1) Data Product Mindset

For data-focused projects, there needs to be a switch in mentality from engineering to data. According to, one of the key focuses should be on adopting a data product mindset, to align the development of data pipelines with business outcomes. Data must be treated as the product, and engineers need to focus on what the value of the data is to the end user. This cannot happen in a vacuum either, as all teams including the consumers, data owners, and professionals need to have input on the development. 

2) Data Integrity 

Businesses need to ensure that data integrity, in the form of validity and accuracy, is quality checked at every step of the pipeline instead of just at the end. This can have many advantages to the end product, as well as reducing the amount of data which needs to be shelved due to issues with formatting, completeness, or consistency. 

See also: Automated Data Pipelines Make it Easier to Use More Data Sources

3) Adaptability

As we said at the beginning, part of the reason for the adoption of data pipelines is due to the large increases in volume and data sources which businesses are seeing. To be on the front foot, businesses need to be adaptable to changes in the data sourcing, as well as new formats and logic. 

4) Non-linear Scalability 

Businesses need to have a long-term plan in place to scale data pipelines, integrating DataOps processes to reduce the amount of effort for the next batch of data pipelines. This can also improve the quality of data collection and the management of said data. 

5) Maintainability 

Maintenance and troubleshooting should be standard practices for businesses deploying data pipelines, with monitoring and detecting systems in place to recognize when something is at fault. Data automation tools should be integrated into the data pipelines to assist engineers in recognizing and dealing with issues. 

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