Snowflake Trail is Snowflake's suite of observability capabilities that enable its users to better monitor, troubleshoot, debug and take actions on pipelines, apps, user code and compute utilizations.

As you can see above, Snowflake Trail utilizes core observability data — logs, metrics, traces, events, alerts, and notifications — to provide comprehensive workload monitoring across AI, applications, pipelines, and infrastructure.

This quickstart is intended to help tie together all the components of Snowflake Trail, and in turn, help you get started with Observability in Snowflake. While this quickstart will walk you through the basics of enabling and viewing telemetry, you will need to dive deeper into each area in order to fully understand Snowflake Trail. So wherever possible, links will be provided to additional quickstarts, documentation, and resources.

Observability in Snowflake comes in two main categories: System Views and Telemetry.

System Views provide historical data about your Snowflake account through views and table functions in the following schemas:

1. The Snowflake Information Schema (INFORMATION_SCHEMA) in every Snowflake database
2. The Account Usage (ACCOUNT_USAGE and READER_ACCOUNT_USAGE) in the Snowflake database
3. The Organization Usage (ORGANIZATION_USAGE) in the Snowflake database

Telemetry data, on the other hand, is delivered exclusively through event tables. An event table is a special kind of database table with a predefined set of columns that follows the data model for OpenTelemetry - a leading the industry standard for collecting and structuring observability data across systems.

This distinction is important because of the default behavior of each:

By default, Snowflake includes a predefined event table (SNOWFLAKE.TELEMETRY.EVENTS) that is used if you don't specify an active event table. You can also create your own event tables for specific uses.

Most telemetry levels can be set at the account, object, or session level. And while many of the telemetry levels can be set via Snowsight, some require using SQL commands for full flexibility.

For this quickstart, we will focus on enabling telemetry at the account level. We will show you both the SQL and Snowsight methods, and use the default table.

Complete one of the following:

You can use Snowsight to set telemetry levels at the account level.

1. Sign in to Snowsight.
2. In the navigation menu, select Monitoring » Traces and Logs.
3. On the Traces & Logs page, select Set Event Level.
4. For Set logging & tracing for, ensure Account is selected.
5. Set your desired levels:
   1. For All Events, select On
   2. For Logs, select INFO
   3. Ensure all other fields show as On.
6. Click Save.

You can see the Set Event Level dialog box below.

1. Open a new SQL worksheet or a workspace.
2. Run the following:

Note that valid and default values are as follows:

A trace represents the complete execution path of a request through your Snowflake workloads. It provides a detailed view of how operations flow through different components, helping you understand performance bottlenecks, dependencies, and execution patterns. Each trace is made up of one or more spans, where each span represents a single operation within the trace (like a SQL query, UDF execution, or procedure call).

Traces help you:

The easiest way to get started with traces is through the Trace Explorer UI in Snowsight:

1. Navigate to Monitoring » Traces and Logs
2. (Optional) Use filters to narrow down the returned results.

You'll now have a list of all the traces for your event table. The Trace Explorer interface shows a list of traces with key information such as Date, Duration, Trace Name, Status, and number of Spans in the trace.

You can now click on any trace to view its spans in detail.

When you click on a trace, you'll see a detailed view showing the timeline of all spans within that trace. This view allows you to:

Clicking on any individual span in the trace opens a sidebar with four tabs: Details, Span Events, Related Metrics, and Logs.

The Details tab shows info and attributes about the selected span, including Trace ID, Span ID, Duration, Type, Warehouse, and more.

The Span Events tab shows details of events recorded within the span.

The Related Metrics tab shows CPU and memory metrics related to the span.

The Logs tab shows logs directly related to the trace.

This detailed information helps you understand exactly what happened during each operation and identify optimization opportunities.

Logs are structured records of events that occur during the execution of your Snowflake workloads. They provide detailed information about what happened during code execution, including informational messages, warnings, errors, and debug information. Logs are essential for troubleshooting issues, understanding application behavior, and monitoring the health of your systems.

Logs help you:

To view logs in Snowsight:

1. Navigate to Monitoring » Traces & Logs
2. Click on the Logs tab to switch from the default traces view.
3. (Optional) Use the filters to find specific logs. For example:
   • Time Range can be set either by using the drop-down or by clicking on the graph.
   • Severity can be used to select specific log levels (DEBUG, WARN, etc).
   • Languages allows filtering by handler code language (Python, Java, etc).
   • Database allows filtering by specific procedures, functions, or applications.
   • Record allows selecting Logs, Events, or All.

By default, you will be shown a list of logs sorted by timestamp.

You can also click on any log entry to bring up a sidebar with more details, including the full log text.

Query History provides a comprehensive view of all SQL queries executed in your Snowflake account. It's one of the most important tools for monitoring, troubleshooting, and optimizing database performance. Query History shows detailed information about query execution, performance metrics, and resource usage patterns.

Query History helps you:

Query history can be viewed as Individual Queries or Grouped Queries.

To view Query History in Snowsight:

1. Navigate to Monitoring » Query History
2. (Optional) Use the filters to find specific queries:
   • Status: Filter by execution status (Success, Failed, etc.)
   • User: Filter by specific users
   • Time Range: Filter by execution time
   • Filters: Various other filters to help you find a specific query.

When you click on any query in the history, you'll see three main tabs with detailed information:

The Query Details tab shows details about the query run (status, duration, ID, etc), the SQL text of the query run, and the query results.

The Query Profile tab provides a visual representation of query execution, which provides critical details for debugging and optimizing complex queries.

For a list of all possible fields, see the documentation here.

The Query Telemetry tab shows the same telemetry data as the Trace Explorer.

Snowsight also provides Grouped Query History, which aggregates similar queries together:

1. Navigate to Monitoring » Query History
2. Click on the Grouped Queries tab

This feature helps you:

By clicking into a single grouped query, you can see detailed information about execution count, duration, and more.

Copy History provides comprehensive monitoring for all data loading activities in your Snowflake account. It tracks operations from COPY INTO commands, Snowpipe, and Snowpipe Streaming, giving you visibility into data ingestion performance, errors, and throughput patterns.

Copy History helps you:

To view Copy History in Snowsight:

1. Navigate to Ingestion » Copy History
2. (Optional) Use the filters to narrow down activity by status, database, pipe, and more.

Each copy operation entry shows details such as status, target table, pipe, data size, and number of rows loaded.

Click on any operation to see detailed information about that operation/target table, such as individual file details and status.

Task History provides monitoring and observability for Snowflake Tasks, which are scheduled SQL statements or procedures that run automatically. Tasks are essential for building data pipelines, ETL processes, and automated maintenance operations. Task History helps you monitor task execution, troubleshoot failures, and optimize task performance.

Task History helps you:

To view Task History in Snowsight:

1. Navigate to Transformation » Tasks
2. (Optional) Use the filters to narrow down activity by status, database, and more.

Task History can be viewed as either Task Graphs or Task Runs.

The Task Graphs view groups related tasks together in a directed acyclic graph (DAG) that shows the relationship between the root task and any dependent tasks. Each row shows the root task name, schedule, recent run history, and more.

Click on any task execution to see detailed information including child task names, status, duration, and more. The real value of this is to be able to see dependencies between different tasks.

The Tasks Runs view shows individual task executions without grouping by parent task.

Clicking into any task run will bring you to the Run History for that task.

Dynamic Tables are a table type that automatically materializes the results of a query and keeps them updated as the underlying data changes. They combine the simplicity of views with the performance of materialized data, automatically managing refresh operations. Dynamic Tables monitoring helps you track refresh performance, data freshness, and resource usage.

Dynamic Tables monitoring helps you:

To view Dynamic Tables monitoring in Snowsight:

1. Navigate to Transformation » Dynamic Tables
2. (Optional) Use the filters to narrow down by refresh status and database.

For each Dynamic Table, we get info such as status, target lag, database, and more.

Clicking on any table will bring you to the graph view for that table.

AI Observability in Snowflake provides monitoring and insights for AI/ML workloads, including Cortex AI functions and model inference operations. As AI becomes increasingly integrated into data workflows, observability helps ensure AI operations are performing reliably and cost-effectively.

AI Observability has the following features:

Congratulations! You have successfully explored the comprehensive observability capabilities available in Snowflake Trail. By following this quickstart, you've gained hands-on experience with the key components that make up Snowflake's observability platform.

Through this quickstart, you have learned how to:

Now that you have explored the basics of Snowflake Trail, consider these possible next steps: