AWS Cloud Operations & Migrations Blog

Visualize data on Snowflake usage and credits using Amazon Managed Grafana

In August 2021, AWS announced Amazon Managed Grafana as generally available (GA), a fully-managed service that’s developed together with Grafana Labs and based on open source Grafana. Enhanced with enterprise capabilities, Amazon Managed Grafana makes it easier to visualize and analyze operational data at scale.

Amazon Managed Grafana is a fully-managed and secure data visualization service that enables customers to instantly query, correlate, and visualize operational metrics, logs, and traces for their applications from multiple data sources. Amazon Managed Grafana is based on the open source Grafana project, a widely-deployed data visualization tool popular for its extensible data source support. Developed together with Grafana Labs, Amazon Managed Grafana manages the provisioning, setup, scaling, and maintenance of Grafana. This eliminates the need for customers to do it themselves. Furthermore, customers benefit from built-in security features that enable compliance with governance requirements, including single sign-on, fine-grained data access control, and audit reporting.

Amazon Managed Grafana is integrated with AWS data sources that collect operational data, such as Amazon CloudWatch, Amazon Elasticsearch Service, Amazon Timestream, AWS IoT SiteWise, AWS X-Ray, and Amazon Managed Service for Prometheus (AMP). It also provides plug-ins to popular open-source databases, external Enterprise data sources like Snowflake, as well as other cloud services. Amazon Managed Grafana lets you easily visualize information from multiple AWS services, AWS accounts, and Regions in a single Grafana dashboard.

Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. Snowflake is an AWS Partner with multiple AWS accreditations including AWS Competencies in Machine Learning and Data and Analytics.

In this post, we explain how to visualize Snowflake usage and billing data with Amazon Managed Grafana. By using Amazon Managed Grafana’s unique architecture we query the data in real-time from Snowflake rather than moving the data and paying for redundant storage and ingestion into Amazon Managed Grafana. Moreover, we demonstrate how to set up custom query dashboards for monitoring your Snowflake usage and billing.

Solution overview

Figure 1. Architecture Diagram for this Snowflake and Amazon Managed Grafana Integration

In this example, we have integrated Amazon Managed Grafana with Snowflake as the data source to build dashboards to query and visualize Snowflake’s credits data from warehouse_metering_history table within Snowflake.

Figure 1: Visualization on Amazon Managed Grafana for Snowflake Credits Utilization

Figure 2: Visualization on Amazon Managed Grafana for Snowflake Credits Utilization

Once Amazon Managed Grafana and Snowflake have been configured, as shown in the figure above, you can visualize the total monthly Snowflake credits. In this example we have set up a Gauge metric. Furthermore, this gauge depicts a red overlay, since Snowflake credits used have reached a preconfigured threshold of 80%. However, the chart on the right depicts monthly Snowflake Credits used per-warehouse.

Walkthrough

Here are quick steps to get started with the Snowflake plugin in Amazon Managed Grafana:

  • Step 1: Set up Amazon Managed Grafana
  • Step 2: Set up the Snowflake Data Source on Amazon Managed Grafana
  • Step 3: Set up Snowflake Dashboards on Amazon Managed Grafana

Prerequisites

For this walkthrough, you should have the following prerequisites:

• An AWS account
• Access to Amazon Managed Grafana with an Enterprise License
• Snowflake account

Step 1: Set up the Amazon Managed Grafana

Refer to Amazon Managed Grafana – Getting Started for information on how to configure Amazon Managed Grafana. Additionally, Amazon Managed Grafana also supports direct Security Assertion Markup Language (SAML) 2.0 integration via external Identity providers, without the need to go through AWS Identity and Access Management (IAM) or AWS IAM Identity Center (successor to AWS Single Sign-On). To use AWS IAM Identity Center with Amazon Managed Grafana, you must also have AWS Organizations activated in your account. If you don’t have it activated already, then Amazon Managed Grafana activates it when it activates AWS IAM Identity Center. If Amazon Managed Grafana enables Organizations, it also creates an organization for you. For more information about Organizations, see What is AWS Organizations.

Figure 2: Amazon Managed Grafana getting started screen

Figure 3: Amazon Managed Grafana getting started screen

Step 2: Set up the Snowflake Data Source for Amazon Managed Grafana

Authenticate into Amazon Managed Grafana environment via AWS IAM Identity Center (successor to AWS Single Sign-On) or SAML 2.0.

Next, to set up the Snowflake data source within Amazon Managed Grafana environment, choose Configuration, Data Sources, Add data source, then search and select Snowflake.

Figure 3: Adding Snowflake as Data source

Figure 4: Adding Snowflake as Data source

Next, under Settings, enter the values for the following fields.

Figure 4: Configuring Snowflake Data source inside Amazon Managed Grafana

Figure 5: Configuring Snowflake Data source inside Amazon Managed Grafana

  • Account name for Snowflake.
  • Username for the service account.
  • Password for the service account.
  • Schema – set it to ACCOUNT_USAGE.
  • Warehouse – provide the warehouse name.
  • Database – set the warehouse name to SNOWFLAKE.
  • Role – provide the respective role from Snowflake.
  • Make sure that the role has imported privilege on the Snowflake database.

For example, you can run this sample query inside of your Snowflake worksheet to grant the required role.

grant imported privileges on database snowflake to <role>;

Next, the User will require a read access to account_usage schema within Snowflake DB to perform the necessary lookups.

For example, you can run this sample query inside of your Snowflake worksheet to enable read access.

CREATE OR REPLACE ROLE SAGEMAKER_ROLE COMMENT=‘Snowflake Grafana Role’;
GRANT ALL ON WAREHOUSE SAGEMAKER_WH TO ROLE SAGEMAKER_ROLE;

Step 3: Set up the Snowflake dashboard on Amazon Managed Grafana

Within the Amazon Managed Grafana environment, choose create a Dashboard.

Next, choose Add an empty and name the Panel ‘Credits Used’ to track the cumulative credits used.

Figure 5: Create a new Dashboard inside Amazon Managed Grafana

Figure 6: Create a new Dashboard inside Amazon Managed Grafana

Next, choose the newly created Snowflake data source from the drop down.

Provide the following sample query in the panel. Adjust the threshold values in the right side under Thresholds settings to reflect the color coding of the data.

SELECT sum(credits_used) as monthy_credits
FROM snowflake.account_usage.warehouse_metering_history
WHERE CONVERT_TIMEZONE('UTC', start_time) >= DATE_TRUNC('MONTH', CURRENT_DATE())

Next, repeat the above steps to add an Empty panel and name the Panel ‘Credits per Warehouse’ to track the credits used per warehouse.

Choose Snowflake datasource from the drop-down at the bottom and supply the following query in the panel:

SELECT warehouse_name, sum(credits_used) as monthy_credits
FROM snowflake.account_usage.warehouse_metering_history
WHERE CONVERT_TIMEZONE('UTC', start_time) >= DATE_TRUNC('MONTH', CURRENT_DATE())
GROUP BY warehouse_name
ORDER BY sum(credits_used) DESC
Figure 6: Query interface inside Amazon Managed Grafana

Figure 7: Query interface inside Amazon Managed Grafana

Since, Amazon Managed Grafana supports time series for more granular and detailed graph visualizations,  you can use the $__timeFilter and $__timeGroup macros.

A sample time series query for visualizing Snowflake credits:

Select sum(credits_used) as credits,
$__timeGroup(start_time, $__interval), warehouse_name
from snowflake.account_usage.warehouse_metering_history
WHERE $__timeFilter(start_time)
GROUP BY warehouse_name,start_time
ORDER BY start_time,sum(credits_used) ASC;

Alerting

You can configure alerts in your Amazon Managed Grafana workspace. In addition, you can enable notifications such as Amazon Simple Notification Service (Amazon SNS), Slack, OpsGenie, PagerDuty, and VictorOps to alert and notify.

In this example, via Amazon Managed Grafana let’s configure an alert on credits usage for a specific Snowflake warehouse and setup a SNS notification which will be triggered, once the pre-configured threshold value is breached.

To create an alert, add an Empty panel and name the Panel for example ‘Generate Credit Usage Alerts per Warehouse’ to track the credits used per warehouse or you can also choose Edit option inside an existing panel to modify the query and add alerts. Select the visualization as Time series to enable the Alert tab.

Choose Snowflake datasource from the drop-down at the bottom and you can supply the following sample query in the panel:

select sum(credits_used) as monthy_credits, $__timeGroup(start_time, $__interval), warehouse_name
from snowflake.account_usage.warehouse_metering_history
WHERE $__timeFilter(start_time) AND WAREHOUSE_NAME='DATALAB’
GROUP BY warehouse_name,start_time
ORDER BY start_time ASC

Next, select the Alert tab to create an Alert and configure the Condition to trigger an alert. An example Condition can be something like, if the average of the query is above a certain value, then trigger an Alert. You can also setup the necessary Notification channel to receive notifications.

Figure 7: Alert configuration inside Amazon Managed Grafana which uses Time Series

Figure 8: Alert configuration inside Amazon Managed Grafana which uses Time Series

To setup Notification Channel, select Alert icon on the left and choose Notification Channels and select New Notification Channel and configure the Notification channel.

Figure 8: Configure Notification Channels under Alerting

Figure 9: Configure Notification Channels under Alerting

If you have enabled service-managed permissions and included Amazon Simple Notification Service (Amazon SNS) as a notification channel for your Amazon Managed Grafana workspace, you only need to provide the SNS topic Amazon Resource Name (ARN) when you create your notification channel. The SNS topic must be prefixed with grafana for notifications to successfully publish to the topic. If you use customer-managed permissions, the IAM role that you supply should include SNS publish permissions for your SNS topic.

Under New notification Channel, provide a desired Name, Type (in this example it’s AWS SNS), Topic ARN and other required parameters, to receive the notification once the defined Threshold on your Alert is met.

Figure 9: SNS Notification Channel configuration under

Figure 10: Configure AWS SNS Notification under New Notification channel

Cleaning up

To avoid incurring future charges, delete the resources, such as Amazon Managed Grafana workspace, Amazon SNS, etc. If you have signed-up for the Amazon Managed Grafana trial Enterprise license, then the license will expire after 30 days.

Conclusion

We’ve demonstrated how you can connect to Snowflake using Amazon Managed Grafana, as well as access credits consumed in your Snowflake account by various virtual warehouses. We also provided an overview on how to setup alerts inside Amazon Managed Grafana so that you’re notified when credit limits exceed warning thresholds to take the remedial action as needed.

Authors:

Bosco Albuquerque

Bosco Albuquerque is a Sr Partner Solutions Architect at AWS and has over 20 years of experience in working with database and analytics products from enterprise database vendors, and cloud providers, and has helped technology companies in designing and implementing data analytics solutions, and data products.

Ravikiran Koduri

RK Sai (Ravikiran Koduri) is an Enterprise Support Lead at AWS. As a technical advisor, he helps Independent Software Vendors (ISVs) to innovate and operate their workloads on AWS at scale. RK is passionate about Machine Learning, Cloud Financial Management and is an evangelist for AWS Deep Racer, presented at multiple events and AWS Summits. When not helping customers, RK is striving to concretize abstract sense of fulfillment by anchoring to his Guru as a seeker and enjoys his time with family on hikes, retreats.

Sunil Ramachandra

Sunil is a Senior Solutions Architect at AWS. As a Solutions Architect , Sunil partners with customers to design and build some of the world’s most scalable, flexible, and resilient cloud architectures. As part of his role, Sunil enables hyper-growth SaaS Independent Service Vendors (ISVs) by innovating and building AWS integrations to enable and accelerate customers success journey on AWS. When not helping customers, Sunil enjoys spending time with his family, running, meditating and watching movies or originals on Prime Video.

Andries Engelbrecht

Andries Engelbrecht is a Principal Partner Solution Architect at Snowflake working with strategic partners. He is actively engaged with strategic partners like AWS supporting product and service integrations, as well the development of joint solutions with partners. Andries has over 20 years of experience in the field of data and analytics.