Lightweight Observability Mechanism for Data Pipelines

Introduction

In this case study, we will explore how a company solved its data observability challenges by building a lightweight observability mechanism in its data processing pipeline. The company faced several issues due to a lack of visibility into its data pipeline, which resulted in delays, increased costs, and data integrity issues.

Issues Faced

The company had a data processing pipeline that processed more than 1 million records every day from three different sources. The pipeline was based on a cron job that ran periodically to process the data. However, the company faced several issues due to a lack of observability in its pipeline.

One of the significant issues they faced was a delay in knowing about data vagaries. For example, delays in alerting the data vendor when data did not arrive on time or processing cost overruns when data abnormally increased. Additionally, they were not aware of a gradual increase in data that eventually became unusually high in a period of 2-4 weeks. This lack of visibility into their data pipeline resulted in data integrity issues, which they could only identify after the damage was already caused.

What was done?

To solve their data observability challenges, Pevatrons built a lightweight observability tool into their data processing pipeline. Pevatrons used the following building blocks:

Airflow as an orchestration tool: We broke down their monolithic cron job into multiple logical tasks using Airflow as an orchestration tool with a Directed Acyclic Graph (DAG). Each task would check data integrity before passing it on to the next task in the pipeline.

Apache Superset dashboard: The company used Apache Superset to create a dashboard for visualizing data. We set up the dashboard to show aggregate data and used the lesser-known feature of Superset to alert any variations in the data.

Slack integrated alert mechanism: The company uses Slack for internal communication. So we integrated Slack into their pipeline and dashboard to receive immediate notifications of any data integrity issues or anomalies.

AWS CloudWatch for log analysis: The logs from the tasks in the DAG of Airflow were fed to AWS CloudWatch for easy analysis.

The lightweight observability mechanism was built into the pipeline itself, enabling the company to monitor the pipeline's data integrity in real-time.

What Improved?

After implementing the lightweight observability tool, the company significantly improved its data processing efficiency. After data processing was complete, they observed a more than 50% reduction in surprises. Additionally, the company could identify data integrity issues in real-time and take corrective actions, preventing the damage caused by these issues.

Conclusion

Data observability is crucial for any data processing pipeline, and it is essential to identify and resolve issues before they cause significant damage. In this case study, we saw how a company faced several challenges due to a lack of observability in its data processing pipeline. However, jumping and purchasing a grand data observability tool is unnecessary unless the system is vast and complex. We implemented a lightweight observability mechanism that helped them identify data integrity issues in real-time and take corrective actions. The tool was built into the pipeline, making it easy for the company to monitor the pipeline's data integrity in real-time.