Many organizations today are exploring cloud data warehousing. At this juncture, Snowflake is undoubtedly becoming the choice for many organizations. The reason is that it is a user-friendly and scalable platform. Nevertheless, when power comes, responsibility also comes together as a free addition. Responsibility becomes even more important with logging and monitoring. If you wish to ensure that the Snowflake atmosphere of your organization is healthy, it needs an efficient strategy developed upon the best practices and the right tools.

Here, you can understand how important Snowflake Monitoring is. Also, you can learn about the Snowflake logging tools. From this blog, you can also gain some practical tips on Snowflake performance monitoring. With these tips, you can keep your Snowflake environment safe.

Importance of Monitoring and Logging in Snowflake

You might be developing data pipelines, loading a huge database, or running ad hoc queries. In any case, observation is important. With monitoring and logging, you can achieve the following in Snowflake:

• They will help spot hindrances in the performance of queries
• You can keep an eye on user activity
• You can gain data access for compliance
• They will help you diagnose errors and investigate failures
• You can optimize costs by gaining insights into computing usage
• You can ensure security through audit logging

The good thing about Snowflake is that the platform offers native tools to monitor and log. Also, the platform integrates well with third-party platforms to help you gain deeper insights.

Essentials for Snowflake Monitoring

Now, you know the importance of Snowflake Monitoring. But you might not be aware of what is needed for monitoring. Here are some important things required for Snowflake Monitoring.

Query Performance Monitoring

For monitoring query performance, Snowflake’s SQL and UI functions provide robust capabilities.  Here are some capabilities you can use for monitoring query performance in Snowflake:

• Query History View: In Snowflake, you can filter queries by different categories. Examples include warehouse, duration, status, and user. With these views, you can spot failing or long-running queries.
• Query Profile: This is a built-in feature in Snowflake. With this, you can visualize the execution plan of a query. Using this facility, you can see the stages of query, like data aggregation, joining, and scanning.

Warehouse Monitoring

It should be stated that warehouses are the compute engines in Snowflake. With this monitoring, you can achieve the following:

• Credit Usage: You can judge the cost implications of every warehouse
• Concurring Scaling: With this, you can check whether any new cluster has been added automatically.
• Load and Queue Time: With this feature, you can understand if users await resources.

Storage Monitoring

With the account usage schema in Snowflake, you can keep an eye on:

• Fail-safe storage consumption
• Time travel storage usage
• Table sizes

When you track storage, you can control costs. Also, it will help with optimizing data retention policies.

Snowflake Logging Tools

Not only monitoring but logging is equally important in Snowflake, as you know. The good thing about Snowflake is that the platform offers comprehensive logging options. These are important not only to track activities but also to troubleshoot issues and ensure compliance.

Access History Logs

For enterprises and editions above this, Snowflake offers Access History to monitor data access. With this facility, you can:

• Audit sensitive table usage
• Spot potential unauthorized access
• See who accessed what data and when

Query and Login History

For security auditing and operational insights, Login and Query history are important metrics. You can find these under the account usage schema of Snowflake:

• Query History will let you track queries, resources used, and execution time
• With Login History, you can review every login attempt, like success or failure, login method, and IP address.

Event Table

Recently, Snowflake launched Event Tables into its platform. It offers a stream-like interface to system logs. This enables integration with event-driven architectures and near real-time monitoring.

Information Schema Views

You can achieve a more granular tracking with:

• Query history in the information schema
• Load history
• Pipe usage history

With these, you can track data loading errors and ingestion jobs.

Third-Party Snowflake Monitoring & Logging Tools

Indeed, native features in Snowflake are powerful. Nevertheless, the integration of third-party tools offers advanced analytics and centralized dashboards. Here are a few Snowflake logging tools and monitoring integrations:

DataDog

• Leverage logs for anomaly detection
• Set alerts for unusual credit usage or slow queries
• Visualize Snowflake metrics alongside your whole infrastructure

Splunk

• With this integration, you can build custom dashboards for performance metrics, failed logins, and user activity
• Correlate Snowflake data with infrastructure and application logs
• Ingest Snowflake logs with the help of JDBC/ODBC or Snowpipe.

New Relic

• This integration will help create alerts for spikes in traffic and high-cost queries.
• With this tool, you can monitor the time required for executing queries. Also, it will help evaluate warehouse health and compute usage.

Sigma Computing/Tableau/Looker

Indeed, these are business intelligence tools, as you know. However, you can use them for self-service monitoring dashboards over Snowflake data. You can do this, particularly by querying account usage views.

Snowflake Performance Monitoring Tips

You should follow a proactive approach to get excellent performance from your Snowflake environment. Understanding this, here are tried and tested tips for Snowflake performance monitoring:

Use Resource Monitors

With resource monitors, you can prevent cost overruns. This can be done by monitoring credit consumption across warehouses. For effective use of resources, you can:

• Set thresholds like 80% of the monthly credit budget
• Alert admins or suspend warehouses automatically.

With these moves, you can ensure that compute usage does not go out of control.

Monitor Query Pattern

With the help of query profile and query history, you can evaluate:

• Queries with large outcome sets
• High-spills queries that denote excessive disk usage
• Long-running queries

To optimize, you can follow the tips given below:

• Materialize frequently used aggregations or joins
• Use clustering keys on large tables

Track Queuing and Concurrency

In case your users report delays, you should monitor the queuing time. You can:

• Distribute user jobs across different warehouses
• Decide on the right-sized warehouses based on trends in loading
• Scale automatically by adding a multi-cluster warehouse.

Optimize Data Loading

To spot slow ingestion and load failures, you can use Load-History. Here are the other things you can do here:

• Before loading, partition the staging data
• Keep optimal file formats like Parquet and compress files
• For continuous loading, you can use Snowpipe

Automating Monitoring and Logging with SQL & Alerts

With Snowflake, you can build an automated check using scheduled tasks and alerts. Gather more information here:

Scheduled Tasks

With this, you can create tasks to run hourly or daily.

Alerts with Snowflake + Email/Webhooks

Indeed, Snowflake does not support alerts natively. Nevertheless, you can use the following:

• Snowflake + dbt + Airflow: This combo will help you create orchestrated alert pipelines
• AWS Lambda or Azure Functions: This will help you process outcomes from Snowflake queries. Also, you can send Slack messages or emails.

Best Practices for Monitoring and Logging in Snowflake

You will be intending to stay on top of your Snowflake environment. To achieve this, you can follow the practices given below when monitoring and logging in the environment:

Get Help from Native Views

You can use the account usage and information scheme for detailed monitoring available via BI tools and SQL.

Track Audit Trails

For compliance and security, make sure to enable login history and access history tracking.

Visualize your Metrics

Do not keep logs hidden in tables. You can use the dashboard via Grafana, Looker, or Tableau for better insights.

Automate Where Possible

You can automate monitoring checks. This can be done with integration, task, or external schedulers.

Set Alerts for Anomalies

To get alerts, you can integrate Snowflake with third-party tools. You can set alerts when storage usage exceeds thresholds or query times spike.

Educate Users

Motivate analysts and developers to use Query Profile and gain insights into performance best practices.

Final Thoughts

Snowflake monitoring and logging are not just about checking the performance of the platform. Rather, they are about proactively ensuring performance, maintaining security, and controlling costs.

With the help of external integrations and native tools, following performance optimization tips, and setting up automated tasks, you can develop a resilient observability strategy. These practices become even more important when your data workloads scale.

You might be a data analyst, engineer, or admin. In any case, you should take control of Snowflake logging tools. Also, you should master Snowflake performance monitoring. Only then will you get dividends in peace of mind, cost savings, and operational efficiency.