# Building a Centralized Alerting Framework for Data Quality Monitoring and Incident Management

DevFeed: [Building a Centralized Alerting Framework for Data Quality Monitoring and Incident Management](<https://devfeed.tech/articles/building-a-centralized-alerting-framework-for-data-quality-monitoring-and-incident-management-30514.md>)

Original publisher: [Read original article](<https://medium.com/helpshift-engineering/building-a-centralized-alerting-framework-for-data-quality-monitoring-and-incident-management-2f90d93a65b5?source=rss----3229f31ca4f4---4>)

Author: Manav Mehta

Published: 2026-06-18T07:11:45Z

Content type: article

Language: en

Sources: [Helpshift](<https://devfeed.tech/sources/helpshift.md>)

Topics: [Data Quality](<https://devfeed.tech/topics/data-quality.md>), [incident management](<https://devfeed.tech/topics/incident-management.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [DataOps](<https://devfeed.tech/topics/dataops.md>)

Tags: [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [data-quality](<https://devfeed.tech/tags/data-quality.md>), [email](<https://devfeed.tech/tags/email.md>), [incident-management](<https://devfeed.tech/tags/incident-management.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [notifications](<https://devfeed.tech/tags/notifications.md>), [observability](<https://devfeed.tech/tags/observability.md>), [pipelines](<https://devfeed.tech/tags/pipelines.md>), [slack](<https://devfeed.tech/tags/slack.md>), [snowflake](<https://devfeed.tech/tags/snowflake.md>)

## AI overview

This article describes the design of a centralized alerting and incident management framework for data quality and pipeline monitoring. The framework uses Snowflake's native Alerting capabilities with Email, Slack, and Splunk On-Call integrations to detect issues, notify the appropriate engineers, escalate critical incidents, and provide actionable context.

## Source excerpt

Before We Knew Better As our data platform grew, so did the number of pipelines, scheduled tasks, and data quality checks running every day. While Snowflake provided a reliable platform for storing and processing data, operational monitoring was fragmented across multiple systems. Data quality failures were often discovered only after downstream reports showed inconsistencies. Pipeline issues sometimes required engineers to manually inspect logs, query tables, and trace execution paths before identifying the root cause. The challenge wasn't detecting failures -- we already had mechanisms to identify them. The real challenge was ensuring the right people were notified quickly, with enough context to take action. Questions during on-call incidents were often similar: Did the pipeline fail or was data simply delayed? Which validation check triggered the alert? Who should respond to the issue? How can we ensure critical failures don't get missed overnight? As the number of pipelines increased, manually monitoring these failures became increasingly difficult. We needed a centralized alerting framework. What We Actually Needed Our goal wasn't simply to send more notifications. We wanted a system that could: Detect data quality issues automatically Notify engineers through channels they already use Escalate critical incidents to on-call responders Provide actionable context instead of generic failure messages Scale across multiple pipelines and monitoring use cases Most importantly, we wanted to keep the solution as close to the data platform as possible. Since our monitoring logic already lived in Snowflake, it made sense for the alerting framework to live there as well. The Architecture We Chose To address these challenges, we designed a centralized notification and incident management framework using Snowflake's native Alerting capabilities, combined with Email, Slack, and Splunk On-Call integrations. Rather than introducing another monitoring platform, we chose to build