From Cost to Clarity: Reducing Observability Spend While Elevating Pipeline Insight

A team within a technology-forward quick-service restaurant chain manages data across key customer touchpoints including loyalty programs, mobile orders, and digital engagement. With new digital platforms and an expanding data landscape, this team needed to ensure data quality and pipeline reliability while managing the cost of observability, the tools and processes that help monitor system health, detect issues, and keep data flowing smoothly. They also wanted to prevent their team from becoming overwhelmed by the increasing amount of data requiring oversight.

The team already relied heavily on a vendor product to monitor data health across more than 25 datasets. While effective, this solution could be costly and sometimes led to alert fatigue. With 18 new tables capturing email and SMS push data to be added, the burden would grow, so the team needed a more scalable and affordable approach.

Elder Research explored a hybrid approach that blended the current vendor’s strengths with existing tables and an internal observability solution built in Databricks for the new ones. Leveraging a pre-existing job status table, we developed lightweight observability checks that captured new row counts and change data activity without the overhead of daily or even hourly query loads. A dashboard was built to visualize patterns and identify anomalies in real time. Alerts were set for gaps in expected activity, particularly tailored to the irregular cadence of email and push campaign data.

This hybrid approach delivered clear benefits:

• Reduced compute costs (at an estimated annual savings of $5,000) by configuring data observability using native Databricks functionality.
• Created and implemented a dashboard to visualize the daily load count of all email and push data, leading to a more efficient overview of table health.
• Empowered the team to scale monitoring without sacrificing data quality or team efficiency.
• Shared out findings to other teams interested in reducing costs and using Databricks more effectively for data observability.

By combining internal tools with targeted automation, the team unlocked a flexible and cost-effective observability framework. Innovation doesn’t always require more software or even compute power—just a deeper look at the tools you already have.