It is a common misconception that pipelines for data, once constructed need no more attention. This article outlines the crucial need for regular maintenance to keep track of modifications in the data sources and structure and ensure that pipelines are functioning effectively and produce high-quality data.
Data pipelines gather data from different sources, then combine them, use a variety of transforms, and then combine them into one single reliable source. A reliable pipeline will always produce error-free data only if the data sources it was designed to work with remain identical, which isn’t always the case. Most pipelines are constructed based on pre-defined schemas for the sources, including the names of columns and data types, as well as how many columns there are. Any slight change to the data standards could cause a disruption to the pipeline and disrupt the whole process.
Data pipeline maintenance is an iterative process that requires constant monitoring of a variety of elements of the pipeline to make sure they’re working as planned. Maintenance of pipelines is an essential element of the data ecosystem since a broken pipeline can lead to poor quality data that can affect business reports as well as decision-making. However, before we proceed, let’s discuss the reasons why maintenance is necessary.
Why does data change?
In a world that is constantly changing, changes are the normal process. Companies are constantly changing their data models in order to grow their businesses to meet the needs of their customers. Data changes within an business usually happens because of:
- Changes in the structure of applications: New input fields are added to the front-end of the application, making new columns for the tables of data. For instance, Google Ads updates their API.
- Manual modification of tables: Some team members might create new columns or alter the types of data or column names based on personal needs. They might even alter the process of creating a table, or view, thereby altering the whole schema. As an example, it is possible to add the new field to your CRM.
- Data standards change: Certain changes in data standards, like those proposed by FHIR for medical records, could need to be added or removed from a model of data.
- Changes in data from external sources: The APIs for third-party data sources could modify their interfaces, which could result in a different format.
The changes cause disruption to the data pipeline by adding elements that weren’t present at the time of construction. Some changes are not predictable and are only discovered when trying to fix a pipeline that is broken.
Why should you be concerned?
The data pipeline is an linear structure comprising intermediate and transformation views and the final destination. A slight alteration in source data could cause a problem for the entire pipeline, as the error spreads downstream, impacting all models that is in the middle.
A damaged data pipeline could be the cause of two problems. The pipeline may not break, but instead begins to dump incorrect data, without anyone being aware. In this scenario, companies continue to operate on inaccurate information, which can hurt their revenue and customer base. The errors aren’t usually discovered until damage is caused.
Another scenario is when pipelines begin to produce errors because of a logic mismatch. While the error is immediate, identifying and fixing the issue can take some time. The result is data outages that result in missed business opportunities as well as customers. This raises doubts about the other data operations while business managers lose faith in the data-driven decision making process.
Many businesses don’t realize the importance of maintaining regularly pipelines until it’s too late. Let’s talk about a few methods to maintain data pipelines.
How do you ensure that data pipelines are secure?
Data operations that are modern (DataOps) adhere to the same processes as regular DevOps and include new tools as well as automated tests. These methods allow data teams to adhere to practices that help keep the quality and health of pipelines and data. These methods include:
- Utilizing tools to manage change data capture: change data capture (CDC) refers to the use of special tools to monitor data for significant changes. These tools assist in determining how the changes will affect existing pipelines and then implementing the change wherever it is needed.
- monitoring data sources: Monitoring of external APIs is done using rigorous validation tests to identify any changes in the format of the interface. A quick detection of any changes could save you a lot of time in debugging.
- Internal Tests for Data: Data tests are similar to unit tests in programming. They test the logic of scripts and schemas for data to ensure that all pipeline components are exactly as they should be. The tests are executed at different locations so that there is no chance of a mistake going unnoticed.
- issuing alerts: Implementing alert mechanisms that include all validation tests as well as monitoring techniques to inform Data engineers of any errors promptly.
- Choosing the right team to maintain: The best team that can fix issues with data is the one that designed the pipeline first. They are experts about the functioning of different components and are able to apply changes in the shortest amount of time feasible. This means that there is a minimum of downtime, which means more efficient damage management.
These practices help create a solid data infrastructure within the organization which results in more efficient workflows and precise data.
Conclusion
Maintaining data pipelines isn’t an event that happens once; rather, it is a constant requirement. The changes in the data sources, structures and business requirements could interrupt pipelines, impacting the quality of data as well as making decisions. Regular monitoring of updates, maintenance, and monitoring are crucial to ensure the flow of data smooth and stable as well as ensuring the security of business operations.
