Today, businesses of all sizes face an escalating demand for digital services across their customer engagement avenues as well as in their operations. As a result, the volume of data that flows through different business systems is also growing at an exponential rate, which mandates the use of powerful and efficient data pipelines to streamline data management. A data pipeline is the virtual equivalent of a factory assembly line for an enterprise’s digital ecosystem.
It takes data from different sources to various stations (consuming services) for executing specific instructions or tasks and further takes the processed insights to decision systems for driving actions. Studies show that the global market for data pipelines will be worth over USD 33.87 billion in value by 2030.
The rapid rise of services that consume data for their execution, ranging from big data to streaming, and even AI or machine learning services, is guaranteed to put additional stress on enterprise data pipelines. The volume and veracity of data handled can often create avenues of failure for data pipelines. In most cases, failure is attributable to engineers not being aware of the key factors driving challenges in a data pipeline.
Why do Data Pipeline Failures Happen?
Despite being a core pillar of any data-driven business, enterprises often face scenarios where their data pipelines fail when deployed in production. It is important to be aware of what caused this failure. Here are some of the top culprits:
Unplanned Schema Changes
Most downstream data operations and processes are often configured to rely extensively on upstream data parameters. When the upstream data encounters sudden changes in its schema without a planned change strategy, it can cause chaos and disruption in one or several downstream services that consume insights from the upstream data.
For example, a change of data type or format or even a renaming of fields in an upstream dataflow can cause a further downstream process to be left unable to execute their core data logic due to a mismatch in expected data parameters. The actual parameter needed may be present, but due to a name change of the field, it could be missed by the downstream service as it was programmed to work on the previous data schema only.
Unexpected Data
Like how data schema changes impact downstream services, the sudden entry of new data types not accustomed to the existing data pipeline configuration can significantly impact its functioning. The pipeline will not be able to correctly supply the new data type or stream to the intended service owing to a lack of clarity about its characteristics or composition. The same situation occurs when businesses' systems generate data streams inconsistently due to their glitches or issues.
When such streams enter the pipeline, they can disrupt the configured orchestration of dataflows within the pipeline and can create congestion or erroneous supply to downstream services. In the event of downstream services like analytics engines receiving an erroneous or unexpected data input, the consequences can be too harsh for the business as critical decisions may be impacted negatively.
Performance Troubles
The exponential growth of digital services can eventually create a flow of data so high that the pipeline may experience slowdowns. They may exhibit lower performance owing to being starved of enough memory, computing power, or even bandwidth in their carrier network.
Without a clear resolution of the performance drop, the issue might escalate as data volume scales up, leading to major consequences.
How can Data Pipeline Failures be Avoided?
The common data pipeline failures outlined above have the potential to disrupt key business processes. However, they can be easily managed with a few best practices and tips that engineers can implement. Let us explore two of the most important ones that can address almost all major failure reasons:
Real-time data quality and schema checks
Implementing a real-time automated data quality and schema monitoring system can ensure that all upstream data flows are validated for any inconsistencies such as missing entries, format mismatches, schema variations, etc. Furthermore, such automated checks should be implemented across further downstream processing stages as well to ensure that the correct data is supplied to the entire data pipeline journey without any integrity risks. All schema changes need to be properly documented so that integrating them into the pipeline always leaves a trace that can be used as a reference in future scenarios where new data structures are to be introduced into the data ecosystem.
Auto-scaling and performance monitoring
To prevent performance bottlenecks, enterprises can implement auto-scaling solutions that track pipeline performance attributes and automatically provision more CPU, Memory, or other computing resources to help the pipeline meet scalability requirements when encountering higher data volumes. This will prevent slowdowns and ensure critical business systems can make data-driven decisions and operations without delays.
Building Observability into Pipelines is the Key
Most of the issues that lead to data pipeline failures are caused by engineers' poor visibility in various aspects like deviations in data types, data drifts, performance drops, etc. Before letting such issues affect data integrity, enterprises need to enable better observability into their core data pipeline operations. In this context, they need to pick the right tools, set up the best observability practices, and ensure constant monitoring is in place for continuous success. Getting such a strategy for observability right requires expert guidance that only an established technology partner like Wissen can provide. Get in touch with us to learn more about how your enterprise data pipeline can be strengthened to meet the needs of your business today and in the future.
