Why data pipelines need to be maintained

A common misconception is that data pipelines, once built, require no further attention. This article underscores the critical need for ongoing maintenance to address changes in data sources and structures, ensuring the pipelines continue to function effectively and deliver quality data.

Data pipelines collect data from various sources, combine them, apply several transformations, and form them into a single source of truth. A good robust pipeline will consistently deliver error-free data, but only as long as the sources it is designed for stay the same, which is rarely the case. Most pipelines are built assuming fixed schemas for the sources, which include column names, data types, or the number of columns. The slightest change in these data standards can break the pipeline, disrupting the entire process.

This makes data pipeline maintenance an iterative process that requires constant monitoring of several pipeline elements to ensure they are working as expected. Pipeline maintenance is a crucial part of the data ecosystem, as a broken pipeline leads to poor data quality, which impacts business reports and decision-making. But before moving forward, let’s talk about why maintenance is required.

Why does data change?

In an ever-evolving world, change is the natural order of things. Companies must constantly change their data models to evolve their business and accommodate customer needs. Data change within the organization usually occurs due to:

Change in application structure: New input fields are added to the front-end, creating new columns in the data tables. For example, Google Ads update their API.
Manual alteration of tables: Some team members may create new columns and change data types or column names for personal requirements. They may even change the logic behind creating a table or a view, which may change its entire schema. For example, adding a new field in your CRM.
Change in data standards: Certain changes in data standards, such as those by FHIR for medical records, may require adding or removing elements from a data model.
Change in external data sources: Third-party data sourcing APIs may change their interface, resulting in a changed format.

These changes break data pipelines by introducing elements that were not present at the time of building. Many changes are unpredictable and only noticed while debugging a broken pipeline.

Why you should care?

A data pipeline is a linear system of transformations, intermediate views, and final destinations. The slightest change in the source data can corrupt the entire pipeline as the error propagates downstream, affecting every model in the middle.

A corrupt data pipeline can have two possible consequences. Either the pipeline does not break but rather starts dumping erroneous data without anyone noticing. In this case, the businesses continue to operate based on false information, damaging their customer base and revenue. Such errors are usually not diagnosed until the damage is done.

The other case is that pipelines start producing errors due to logic mismatch. Although the errors are immediately apparent, diagnosing and fixing the problem can take a while. This causes data downtimes resulting in missed business opportunities and customers. It also raises questions about other data operations, and business leaders lose confidence in data-driven decisions.

Most businesses do not realize the importance of frequently maintaining pipelines until its too late. Let’s discuss a few ways data pipelines are maintained.

How are data pipelines maintained?

Modern data operations (DataOps) follow the same routines as regular DevOps, which include using modern tools and automated testing. These measures allow data teams to follow practices that help maintain the health and quality of data and pipelines. Some of these practices include:

Using tools for Change Data Capture: Change Data Capture (CDC) refers to using specialized tools to monitor data for any significant changes. These tools help detect the effect the change will have on existing pipelines and implement it everywhere it is needed.
Monitoring Data Sources: External APIs are monitored with strict validation tests to detect any change to the interface format. Early detection of these changes can save a lot of debugging time.
Internal Data Tests: Data tests work similarly to unit tests in programming. They test script logic and data schemas to assert that all pipeline elements are as expected. These tests are implemented at multiple places so that no error goes undetected.
Issuing Alerts: Implementing alert mechanisms with all validation tests and monitoring methods to notify data engineers of any errors in a timely manner.
Employing the correct team for maintenance: The best team available to fix data-related issues is the one that built the pipeline in the first place. These engineers have complete knowledge of the working of different elements and can apply fixes in the shortest time possible. This ensures minimum downtimes, hence more efficient damage control.

These practices create a robust data infrastructure within the organizations, resulting in smoother workflows and accurate data.

Conclusion

Maintaining data pipelines is not a one-time task but an ongoing necessity. Changes in data sources, structures, and business needs can disrupt pipelines, affecting data quality and decision-making. Regular monitoring, updates, and maintenance are essential to keep data flows smooth and reliable, safeguarding the integrity of business operations.