How Can You Implement Data Observability Best Practices for Snowflake? | A 2026 Implementation Guide

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by Heather Devane, Lead Content Strategist, AtlanLast Updated on: February 20th, 2026 | 13 min read

Quick answer: What are data observability best practices for Snowflake?

Data observability best practices for Snowflake revolve around leveraging native metadata and the Snowflake Trail telemetry framework to continuously capture metrics, logs, and traces and monitor system health.

This builds end-to-end visibility of your Snowflake data estate and optimizes the overall performance, quality, reliability, and governance of your data and AI assets.

Best practices for Snowflake data observability

  • Leverage Snowflake Trail for automated telemetry: Use the Snowflake Trail suite (Query History, Event Tables, Alerts and Notifications) to capture logs, metrics, and traces.
  • Correlate compute metrics with data volume: Use Snowflake Trail metrics to monitor CPU and memory utilization during processing tasks.
  • Leverage native Data Metric Functions (DMFs): Schedule automated checks for freshness, uniqueness, and null rates directly on your tables.
  • Automate schema drift and volume monitoring: Set up automated alerts for anomalies in row counts or structural changes in tables to catch failures early.
  • Use Snowsight Log Explorer and Trace Viewer: Use these built-in visualization tools to monitor and trace pipelines, apps, resource usage and spot performance bottlenecks in complex DAGs visually.
  • Define pipeline criticality and SLAs: Classify your data assets as critical or non-critical to prioritize troubleshooting efforts and support high-impact decisions.
  • Integrate with incident management via OpenTelemetry: Export Snowflake Trail signals to tools like Slack, PagerDuty, Grafana, Metaplane, and Microsoft Teams.
  • Establish end-to-end lineage for impact analysis: Use a metadata control and context plane like Atlan to map data flows from external stages through Snowflake and into your BI layer, enabling you to pinpoint the root cause of quality issues instantly.
  • Export telemetry to a unified context plane: Use the OpenTelemetry standard to stream your Snowflake Trail events to a metadata platform like Atlan, connecting technical execution signals to the downstream business assets they impact.

Below, explore: why observability matters for Snowflake, Snowflake Trail foundations, event table architecture, automated monitoring implementation, quality validation patterns, and alerting best practices.

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Why does data observability matter for Snowflake environments?

Data observability in Snowflake is essential because it moves the focus from simple infrastructure uptime to the actual reliability of the data assets and the code that produces them.

Invisible performance degradation


One of the greatest challenges in modern Snowflake environments is the complexity of custom code. As organizations migrate legacy Spark or Hadoop workloads to Snowpark, they often encounter a “visibility gap.”

“When working with Snowpark UDFs, some of the logic can become quite complex. In some instances, we had thousands of lines of Java code that needed to be monitored and debugged.” - Nick Pileggi, Principal Solutions Architect at phData Inc. on the need for continuous code monitoring.

This provides a clear “black box” view into the internal execution of sophisticated transformations and multi-stage pipelines.

In addition to pipeline efficiency, identifying inefficient queries and runaway tasks that consume credits without delivering proportional business value can also optimize warehouse costs

AI model drift from stale inputs


Machine learning pipelines assume fresh training data. When upstream sources stop updating, models train on outdated patterns. Freshness telemetry detects staleness within minutes rather than discovering degraded predictions weeks later.

Compliance gaps from untracked changes


Schema modifications expose sensitive columns that should remain masked. Without continuous monitoring, governance policies apply to yesterday’s structures while today’s tables violate regulations unknowingly.

Organizations implementing Snowflake observability report that issue resolution accelerates 10x compared to reactive monitoring. Faster detection prevents cascading failures, keeping dashboards reliable and AI systems functioning.

What is Snowflake Trail and how does it enable observability?

Snowflake Trail captures three core telemetry signals–metrics, logs, and traces:

  • Metrics (The “How Much”): Snowflake automatically generates runtime performance metrics, such as CPU and memory utilization percentages and trends, query concurrency levels, and stored procedures.
  • Logs (The “What Happened”): These are independent, detailed messages capturing the specific state of your code at a point in time. Each log entry captures a specific moment: function entry, variable assignments, conditional branches, error conditions.
  • Traces (The “How it Flowed”): Based on the OpenTelemetry standard, traces group operations into spans. A trace allows you to visualize the end-to-end execution flow of a complex operation, showing exactly which part of a 2,000-line Java UDF is causing a bottleneck.

Automated telemetry with Event Tables


Event tables are the foundational storage layer for Snowflake Trail, and Snowflake automatically routes telemetry from your processing logic into these specialized tables. Organizations use this data to optimize warehouse sizing, identify expensive queries, and understand actual data consumption patterns.

Once an event table is created and associated with an account, it automatically captures data from all supported objects, including Snowpark functions and stored procedures, without requiring individual configuration for every task.

Event table structure and querying

Event tables store telemetry in columnar format optimized for analytical queries. Standard columns include:

  • TIMESTAMP: When event occurred
  • SEVERITY_TEXT: Log level or trace span type
  • SCOPE: Source object (procedure name, UDF identifier)
  • RECORD: JSON payload containing telemetry details
  • RESOURCE_ATTRIBUTES: Metadata about execution context

Query event tables using standard SQL. Example finding recent errors:

SELECT timestamp, record['message']::VARCHAR AS message
FROM SNOWFLAKE.EVENTS
WHERE severity_text = 'ERROR'
AND timestamp >= DATEADD(hour, -1, CURRENT_TIMESTAMP())
ORDER BY timestamp DESC;

Organizations build custom dashboards by aggregating event table data, creating organization-specific views of system health.

Retention and storage considerations

Event tables consume storage like regular tables. Implement retention policies balancing historical analysis needs against storage costs.

Common patterns:

  • 7-day hot storage: Full telemetry detail for recent troubleshooting
  • 30-day warm storage: Aggregated metrics for trend analysis
  • 12-month cold storage: Compliance audit trails and incident reports

Automated tasks purge old telemetry data based on retention policies, preventing unbounded growth.

Data quality monitoring integration


Snowflake Trail works alongside Data Metric Functions to provide quality observability. DMFs validate data expectations while Trail telemetry reveals which quality checks failed, when, and why.

This combination enables root cause analysis. When quality issues occur, teams trace validation failures back through pipeline execution logs to identify transformation problems or source data issues.

SQL-driven troubleshooting


Because telemetry is stored in standard Snowflake tables, you can run SQL queries to correlate application errors with infrastructure performance. For example, you can join an event table with the QUERY_HISTORY view to identify which specific SQL statement caused a Java UDF to exceed its memory limit.

OpenTelemetry compliance


By adhering to OpenTelemetry (OTel) standards, event tables ensure that logs and traces are structured consistently. Teams can leverage collected data through Snowsight dashboards, or export to external platforms like Atlan, Grafana, Metaplane and other systems without complex data transformation.

How does Snowflake Trail account for the five pillars of data observability?

Freshness and volume via metrics


Use automated metrics to track the latency of Snowpipe ingestions and the record count of Dynamic Table refreshes to ensure data is moving at the pace the business requires.

Distribution and schema via logs


Implement custom logging within your Snowpark code to capture when a UDF encounters a null-rate anomaly or a structural change in an incoming JSON payload that would otherwise lead to a “silent” failure.

Lineage via traces


Use distributed tracing to map how data travels through a series of nested stored procedures. This allows you to visualize the dependency graph and identify exactly which transformation step introduced a quality issue before it reached the final reporting layer.

How do you implement continuous monitoring and quality validation for Snowflake data pipelines?

In Snowflake, validation must be embedded into the data lifecycle to ensure that the five pillars are constantly verified.

  • Enable automated quality validation with DMFs: Use the native Data Metric Functions (DMFs) to define “expectations” directly on your Snowflake tables. These functions automatically track metrics like NULL_COUNT, DUPLICATE_COUNT, and FRESHNESS.
  • Set up proactive validation patterns: Instead of checking data after it reaches the dashboard, implement validation at the Silver/Transformation layer. If a DMF detects a breach, you can trigger a task to quarantine the data, preventing “garbage in, garbage out” scenarios for downstream AI models.
  • Ensure continuous monitoring via Snowflake Trail: While DMFs check the data, use Snowflake Trail to monitor the process. By continuously analyzing Event Tables, you can detect if a Java UDF is beginning to slow down or if memory usage is creeping up before a hard failure occurs.
  • Add custom spans for performance measurement: Custom spans capture execution time for arbitrary code segments. Strategic span placement can include external API calls, loop iterations, ETL/ELT operations, etc.

What alerting strategies reduce noise in Snowflake observability?

An observability platform is only as good as its ability to drive action. Effective alerting ensures the right person receives the right information at the right time.

  • Intelligent alerting thresholds: Avoid static thresholds that lead to alert fatigue. Use ML-based anomaly detection (available natively in Snowflake) to identify volume or freshness issues that deviate from historical patterns.
  • Integration with incident management: Export Snowflake Trail signals using OpenTelemetry to tools like Slack, PagerDuty, or ServiceNow. For example, a critical “Freshness” failure in a financial table triggers a PagerDuty incident, while a “Schema Change” in a sandbox environment simply sends a notification to a Slack channel.
  • Routing with ownership context: Use Snowflake Object Tagging to associate tables with specific teams. When an alert triggers, the telemetry metadata includes the owner tag, ensuring the incident is routed directly to the responsible data steward.

How can you scale observability across the enterprise?

As your Snowflake footprint grows to hundreds of databases and thousands of tasks, observability must scale without becoming a cost center.

  • Standardized telemetry templates: Create reusable templates to deploy Event Tables and DMFs automatically whenever a new data product is provisioned.
  • Tiered observability levels: Apply “Full-Stack” observability (Traces + DMFs + Atlan) to Tier 1 mission-critical data, while using “Lite” observability (Metrics + Basic Volume checks) for exploratory environments.
  • Cost-aware monitoring: Implement retention policies for your Event Tables—keeping “Hot” logs for 7 days for debugging and moving “Warm” aggregated metrics to long-term storage for trend analysis.

What role does active metadata and a unified context layer like Atlan play?


While Snowflake Trail provides the “how” and “what,” an active metadata platform like Atlan provides the “who” and “where”–context needed to act on those insights.

Unified visibility into data health and lineage

  • Combine metrics, metadata, lineage, and logs in one active metadata layer to understand both what broke and why.
  • Automated column-level lineage from source to BI tools provides end-to-end impact analysis and accelerates root cause analysis when observability tools raise alerts.

Trust engine for analytics and AI

  • Data observability and data quality are implemented together: observability shows how systems behave; quality defines what “good” looks like, giving teams trusted data for analytics and AI.
  • Atlan’s AI-native governance and metadata lakehouse are explicitly called out by Gartner as critical for AI success and active metadata orchestration.

Best-of-breed observability, unified in one control plane

  • Atlan integrates with leading data observability partners like Monte Carlo, Soda, and Anomalo, surfacing incidents and quality checks directly inside Atlan so teams can triage in context.
  • Organizations can use native Data Quality Studio plus external observability tools without tool sprawl, because Atlan becomes the visibility and communication plane across them.

Proven at scale with modern governance & automation

  • Atlan’s automation-first design (Playbooks, app framework) and AI-led metadata curation reduce manual work like profiling, tagging, and governance change management.
  • Recognized by Gartner and Forrester for innovation, Atlan is the governance and reliability backbone for enterprise customers such as Nasdaq, Workday, and General Motors.

How does governance scale with observability?


Data governance and observability reinforce each other. Observability provides visibility while governance defines policies and ownership.

Modern platforms like Atlan extend Snowflake observability through:

Real stories from real customers: Observability at scale on Snowflake

General Motors: Data Quality as a System of Trust

“By treating every dataset like an agreement between producers and consumers, GM is embedding trust and accountability into the fabric of its operations. Engineering and governance teams now work side by side to ensure meaning, quality, and lineage travel with every dataset — from the factory floor to the AI models shaping the future of mobility.” - Sherri Adame, Enterprise Data Governance Leader, General Motors

See how GM builds trust with quality data

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Workday: Data Quality for AI-Readiness

“Our beautiful governed data, while great for humans, isn’t particularly digestible for an AI. In the future, our job will not just be to govern data. It will be to teach AI how to interact with it.” - Joe DosSantos, VP of Enterprise Data and Analytics, Workday

See how Workday makes data AI-ready

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Moving forward with Snowflake data observability

Snowflake Trail provides zero-setup observability through OpenTelemetry-standard event tables capturing logs, metrics, and traces automatically. Start by enabling telemetry for critical databases, instrument strategic code paths with custom spans, and configure alerts that route issues to responsible owners.

However, technical signals alone are not enough to eliminate data downtime. The true value of observability is unlocked when these signals are integrated into an active metadata platform like Atlan.

Atlan extends Snowflake observability with unified quality, lineage, and governance across your entire data stack.

Book a demo

FAQs about data observability best practices for Snowflake

1. How does Snowflake Trail differ from traditional monitoring?


Snowflake Trail provides automated telemetry collection using OpenTelemetry standards without agent installation. Traditional monitoring requires manual instrumentation and external infrastructure. Trail captures logs, metrics, and traces in default event tables immediately without configuration.

2. Do event tables consume Snowflake storage credits?


Yes. Event tables store telemetry data like regular tables, consuming storage based on volume. Implement retention policies purging old telemetry data automatically. Balance historical analysis needs against storage costs through tiered retention strategies.

3. Can I use Snowflake observability with Datadog or Grafana?


Yes. Snowflake’s OpenTelemetry compliance enables integration with ecosystem tools. Connect Datadog through native Snowflake integration or query event tables directly from Grafana using the Snowflake data source. Export telemetry to any OpenTelemetry-compatible platform.

4. How do Data Metric Functions integrate with observability?


DMFs validate quality expectations while Trail telemetry reveals validation results. When quality checks fail, trace data shows which pipeline stage caused issues. This combination enables root cause analysis connecting quality failures to specific transformations or source problems.

5. What’s the difference between alerts and notifications in Snowflake?


Alerts evaluate SQL conditions on schedules, triggering actions when thresholds breach. Notifications deliver messages to destinations like email, Slack, or webhooks. Alerts commonly trigger notifications, but can also execute tasks or update tables for automated remediation.

6. Should observability run in dedicated Snowflake warehouses?


Yes. Isolate monitoring workloads in separate warehouses preventing observability queries from impacting production analytics. Size warehouses based on check frequency and telemetry data volumes. Small warehouses handle basic validation; complex analysis requires larger compute.

7. How does Atlan help operationalize Snowflake Trail and data observability best practices?


Atlan acts as the active metadata and context layer for Snowflake Trail, transforming raw telemetry into actionable business insights. While Snowflake Trail provides the technical signals (metrics, logs, and traces), Atlan provides the “who, where, and what” by mapping those signals to a unified business glossary and end-to-end lineage.

By integrating Snowflake observability with Atlan, teams can accelerate root cause analysis, bridge the gap between data and code, and scale governance with automated telemetry.

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