#snowflake

#snowflake

The concept of a Snowflake Storage Lifecycle Policy is well-established within the realm of cloud computing. This process has long been utilized by major cloud storage providers. Essentially, it involves a set of procedures and policies designed to manage data archival and retention. When data reaches a predefined archival threshold, it is transferred to more cost-effective storage within Snowflake. Eventually, the data expires and is moved to Snowflake's purging cycle, ensuring efficient and economical storage management.

Customers have been using Spark for a long time to process data and get it ready for use in analytics or in AI. The burden of running in separate systems with different compute engines creates complexity in governance and infrastructure.

Niederman's experience includes building AWS's Global Systems Integrators business to a $10 billion run rate, showcasing his ability to drive enterprise transformation.

The addition of the managed PostgreSQL database to the Data Intelligence platform will allow developers to quickly build and deploy AI agents without having to concurrently scale compute and storage.

Lakeflow and OpenFlow reflect two philosophies: Databricks integrates data engineering into a Spark-native, open orchestration fabric, while Snowflake's OpenFlow offers declarative workflow control.

Adaptive Compute simplifies infrastructure management by automatically adjusting the cluster size and number based on workloads, eliminating the need for user intervention.

It was an unfortunate situation that our customers went through, and we've really pivoted from a shared-security model to more of a shared-destiny model with our customers.