How Observe Uses Snowflake to Deliver the Observability Cloud [Part 2]

Observe leverages Snowflake's efficient, compressed columnar data storage to achieve significant data compression, often reaching 10x the original data size, as part of its Observability Cloud's unique architecture. The platform integrates data from various sources into a Data Lake, transforming it into an interactive Data Graph that enables comprehensive data exploration and monitoring. Using the custom OPAL language, which is more straightforward than SQL, Observe models and transforms data into Datasets that represent both business and infrastructure elements, such as user sessions and Kubernetes pods. These Datasets are classified into Event, Resource, Interval, and Table types, each serving different analytical purposes. Observe compiles OPAL into SQL for use with Snowflake, where Datasets are stored in point or interval tables, optimized for efficient querying and cost-effective storage. Datasets can be accelerated for faster querying based on likely access patterns, with a flexible retention policy, allowing businesses to tailor data retention to their needs. This setup enhances data reliability and performance without imposing limits on data storage capacity, paving the way for the next part of the blog series, which will focus on resource management in terms of resilience, quality, and cost.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.