Platform Update - Query Snowflake Data Using Losant Workflows

Snowflake's data storage and analysis platform is a great option when designing your overall IoT data architecture. Today's release makes it easy to directly integrate Snowflake data with your Losant applications to deliver a variety of use cases:

1. Organizations with existing data in Snowflake can now easily access that data within Losant. This can be used to enrich real-time sensor data with existing enterprise data to deliver more sophisticated business logic within Losant workflows.
2. Snowflake can be used to extend or augment Losant's built-in data storage options. Snowflake's SQL databases can store all kinds of data associated with your IoT applications, including time-series sensor data. Snowflake's SQL support makes it possible to query data in more complex ways compared to Losant's built-in time-series aggregations.
3. Snowflake is a great warm and cold storage option for time-series data. This works well for customers who require long-term storage with the ability to directly query data older than their Losant data retention limit.
4. For organizations pursuing AI on top of their time-series data, Snowflake has several AI/ML features worth exploring.

Snowflake Workflow Node

This release adds the Snowflake Node, which can be used to query or insert data into your Snowflake tables.

The screenshot above demonstrates how to send your sensor data to Snowflake in real time. The Device State Trigger is invoked for all devices with common attribute configuration (allowing them to share the same Snowflake table). The Snowflake Node then uses an INSERT statement to store the incoming attribute data. This is an example of using Snowflake to store a copy of your time-series data as a warm/cold storage option. Storing time-series data in Snowflake also makes it possible to use Snowflake's AI/ML capabilities to perform more complex analysis on your sensor data.

The Snowflake Node supports almost every Snowflake SQL command, which makes it possible to query data in a wide variety of ways.

The screenshot above is showing how Snowflake can be used to aggregate time-series data. This example demonstrates how Snowflake can be used to augment or expand the built-in aggregations provided by Losant's Data: Time-Series Node.

Snowflake Service Credential

Data stored in Snowflake can be sensitive, so to ensure access is as secure as possible, this release adds the Snowflake Service Credential. Credentials stored in Losant have an extra layer of encryption and the underlying keys cannot be directly accessed once they've been saved.

When accessing Snowflake data using Application Workflows or Experience Workflows, this service credential is used for authentication. If you're accessing Snowflake data using Edge Workflows, the Snowflake Node directly accepts the authentication details. (Service Credentials are not supported in Edge Workflows.)

Other Updates

As always, this release comes with several other features and improvements, including:

• A new Workflows tab has been added to the Notebook details screen to easily find all workflows associated with the notebook. The workflow tabs on other resources (Integrations and Resource Jobs) have been improved to include workflows that use associated nodes beyond just triggers.
• The Time Series Graph now supports string attributes in expressions. The most common use case is to graph a 1 or a 0 depending on the value of a string attribute. For example, an expression of {{value}} === 'error' results in a 1 on the graph whenever the value is "error" and a 0 for all other values.

What’s Next?

With every new release, we listen to your feedback. By combining your suggestions with our roadmap, we can continue to improve the platform while maintaining its ease of use. Let us know what you think in the Losant Forums.