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.
Table of contents
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?
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?
- 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.
If you’d like expert guidance in ensuring your pipelines are optimized and well-maintained, you can book a free consultation here.
