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Snowflake treats a database created from a provider share as an imported database. Imported databases are read-only, and Snowflake does not support creating clones of an imported database or its contained schemas and tables. Time Travel is also unsupported for an imported database and its contained objects.

A virtual warehouse’s local data cache is dropped when the warehouse is suspended. An auto-suspend setting of 2 minutes can therefore clear the cache before later reports run, requiring the warehouse to rebuild the cache and causing slower performance. Snowflake recommends at least 10 minutes of auto-suspend for BI and SELECT query warehouses when preserving cache is important.

LOCK_WAIT_HISTORY contains the history of transactions waiting for locks and exposes the blocking queries. QUERY_HISTORY provides the SQL statement and transaction details needed to investigate both the blocked and blocking activity. Snowflake’s documented workflow for examining a long-running statement uses these two Account Usage views together.

For a MERGE in which multiple source rows match a target row for an update or delete, the operation is nondeterministic. ERROR_ON_NONDETERMINISTIC_MERGE defaults to TRUE, so Snowflake returns an error instead of selecting a matching source row arbitrarily. Snowflake MERGE documentation

A newly created table is not automatically available through an existing Snowflake share. The provider must explicitly grant SELECT on EDW.ACCOUNTING.Table_6 to PSHARE_EDW_4TEST. Because the consumer role already has IMPORTED PRIVILEGES on the imported database, it can access the table as soon as the provider adds it to that share.

A paid listing associated with a data share lets a Snowflake provider charge consumers for access and monitor usage of the listing and its associated data share. Because the vendors already have Snowflake accounts and the requirement is to share data rather than deploy an application or collect application telemetry, a paid data listing is the lowest-overhead fit.

Data Vault stores business keys in hubs and descriptive history in satellites. A conditional multitable INSERT can route and fan out a source row to multiple targets in one statement, supporting parallel loading of related hub and satellite rows with the appropriate surrogate-key relationship. This differs from dimensional modeling, which uses dimensions and facts.

Interval-based data refresh is designed for updates on a predefined cadence; Snowflake specifically identifies weekly dataset updates as an appropriate use case. Configuring the interval automates ongoing synchronization without requiring a manual trigger for every weekly update.

Search optimization is most effective for selective filters on a column with roughly 100,000 or more distinct values. External tables are unsupported, and clustering or GZIP compression is not a requirement of the service.

Snowflake creates a historical zero-copy table clone with CREATE TABLE <new_table> CLONE <source_table> AT(OFFSET => <seconds>). An offset of -60*5 specifies 300 seconds before the current time, which is five minutes ago.

SnowSQL uses fail-open OCSP behavior by default, so it permits the HTTPS connection while logging a warning when a valid certificate-revocation response cannot be obtained. Snowflake sends OCSP certificate checks over port 80; blocking that port can prevent the client from reaching the OCSP responder and produce this warning.

The Search Optimization Service stores an additional search access path for enabled tables. Automatic Clustering can also increase Fail-safe storage when reclustering rewrites existing micro-partitions. Multi-cluster virtual warehouse configuration affects compute resources, not storage. Snowflake performance optimization documentation

A temporary table with the same name as a permanent table takes precedence for unqualified references in its session. The first count therefore returns 1. DROP TABLE emp drops the temporary table, after which the permanent table is again visible and its count returns 2.

Snowflake Data Clean Rooms provide a secure collaboration environment in which multiple parties can contribute data and analyze combined data without exposing the underlying raw sensitive data. Providers can control which analyses consumers may perform through approved query templates, enabling governed collaboration with minimal custom data-sharing infrastructure.

The INFORMATION_SCHEMA.LOGIN_HISTORY table function returns Snowflake user login events within a specified recent time range; when no end time is specified, it returns the most recent login events. This makes it suitable for investigating active credential-stuffing activity. The ACCOUNT_USAGE.LOGIN_HISTORY view is intended for longer-term history and can have data latency.

Files land every 10 seconds and must be visible almost immediately with minimal coding. A task has a minimum schedule interval of 60 seconds, so any COPY-plus-task or task-plus-stream approach (B, E) cannot meet the 10-second latency. Snowpipe with auto-ingest (A) uses cloud event notifications to load files continuously as they arrive with essentially no custom code, and a materialized view on an external table (C) exposes the staged data to dashboards and auto-refreshes as new files appear — both low-code, near-real-time. A one-off COPY INTO (D) is manual and not continuous. Therefore A and C.

VALIDATION_MODE is unsupported when COPY INTO <table> transforms data during the load. Selecting specific staged-file columns and mapping them to the target columns is a transformation, so specifying VALIDATION_MODE = RETURN_ALL_ERRORS causes the error. Snowflake COPY INTO <table> documentation

Snowpipe cannot automatically delete staged files after they load successfully. Unneeded files can be deleted periodically with the REMOVE command, or retained storage can be controlled through lifecycle-management rules configured in the cloud storage provider. Snowpipe already maintains file-loading metadata to prevent reloading files with the same name; this is not a separate feature to enable.

Accessing a Snowflake table requires privileges on its containing database and schema, in addition to SELECT on the table. IT_ANALYST_ROLE inherits database USAGE and table SELECT privileges but has no schema USAGE, so it cannot read any table. FIN_ANALYST_ROLE inherits the database access through the role hierarchy and has USAGE on FINANCE_SCHEMA plus SELECT on FIN_TABLE, allowing it to read the Finance table only.

Snowflake tag-based masking policies provide column-level protection for PII by associating masking policies with tags applied to sensitive columns. The policies mask values at query time for unauthorized roles and scale consistently across tagged data.

Zero-copy clones initially share the source table’s micro-partitions and represent an unchanged point-in-time copy; creating a dataset with transformed live data requires materializing the transformed results. Cloning is also not a cross-account writable-copy mechanism. Secure Data Sharing between accounts transfers no data but provides imported objects as read-only, so developers who need a writable dataset in a separate account need data to be copied or otherwise materialized there.

A Business Critical provider can share with an Enterprise consumer only by overriding the default edition restriction. The role performing the operation must have the global OVERRIDE SHARE RESTRICTIONS privilege and ownership of the share (or the CREATE SHARE privilege), and the share must set SHARE_RESTRICTIONS to FALSE.

Zero-copy cloning creates a snapshot of a table, schema, or database at the time the clone is created. The clone has its own lifecycle and can be retained independently while later changes occur in the source, making separately created quarter-end clones suitable for long-term retention. Time Travel is limited to a maximum 90-day retention period.

When an aggregation’s intermediate data exceeds warehouse memory, Snowflake spills data to local or remote storage, substantially slowing execution. Increasing the virtual warehouse provides more memory and local storage capacity, while dividing the processing into smaller steps reduces the amount of intermediate data that must fit in memory at one time.