Spilling to remote storage indicates that the intermediate data produced during query execution (like large joins or sorts) exceeds the available memory of the warehouse. The most effective first step is to optimize the query logic itself to reduce the volume of data being processed in memory. Reviewing the join order and applying filters early can dramatically shrink intermediate results, potentially eliminating the spill. While increasing warehouse size (A) can help, it’s a brute-force solution that increases cost and should be considered after query tuning. Clustering (B) helps with pruning but doesn’t directly address in-memory operations for a single query. Using a CTE (C) is syntactic and doesn’t inherently change the execution plan or data volume.

A Secure View shared via a Snowflake Share is the correct mechanism for this scenario. Secure Views protect the underlying table definitions. When combined with a Share, it allows you to provide read-only access to the result set of the view to an external account without giving them any access to your account’s resources or the base tables. Database replication (A) gives the partner a full copy of a database, which is excessive and doesn’t hide structure. A materialized view (C) is a physical copy and would be managed within your account; you cannot directly share a materialized view with an external account without a Share. Creating a user in your account (D) is insecure and violates the principle of separation, giving the partner internal access.

For large, constantly growing tables where queries frequently filter on a specific column (like a date), implementing automatic clustering is the recommended solution. It physically reorders the micro-partitions based on the defined clustering key (`session_start`), which allows Snowflake to perform efficient pruning. This means queries looking for a recent date range will scan far fewer micro-partitions, improving performance. Search optimization (A) is designed for point lookups (e.g., `WHERE user_id = 123`), not date range scans. A materialized view (C) adds maintenance overhead and storage cost and is better for complex aggregations. Archiving old data (D) changes the data model and requires application logic changes, which is more invasive than clustering.

The key detail is that the 20 SQL statements are *independent*. The bottleneck is likely sequential execution on a single cluster. By splitting them into multiple tasks within a task graph, Snowflake can run these independent tasks concurrently on the same warehouse, utilizing its resources more fully and reducing total elapsed time. This is more efficient and cost-effective than simply scaling up. Increasing warehouse size (A) or using maximized mode (D) makes a single query faster but does not address the parallelism of independent jobs. Multi-cluster warehousing (B) is for handling *concurrent users/queries*, not for parallelizing a single batch job’s internal steps.

The Search Optimization Service (SOS) is specifically designed to accelerate point lookup queries (equality predicates, `IN` lists, subqueries) on large tables. It maintains auxiliary data structures that allow Snowflake to locate the exact micro-partitions and rows containing the key value with minimal scanning. While the result cache (D) would help on subsequent identical queries, it doesn’t optimize the first execution. Automatic clustering (A) helps with range scans and pruning but isn’t optimized for single-key lookups on a large scale. A materialized view (C) is overkill for this simple lookup and adds maintenance overhead.

A stream on the *source* table records all DML changes (INSERTs, UPDATEs, DELETEs) as they happen. By using this stream as the source in a MERGE statement within a scheduled task, you can efficiently apply the exact same changes (inserts, updates, AND deletes) to the target reporting table. This pattern is a standard and robust method for incremental loading with change data capture (CDC). A stream on the target (A) wouldn’t help identify source changes. Change tracking (B) can identify new/modified rows but does not natively track deletes without additional logic. Streams on stages (C) track new files, not row-level changes within data.

The most direct cost-saving measure for a warehouse with a predictable, limited usage window is to explicitly control its state. Suspending it when not in use (outside of 1 AM – 4 AM) stops all compute charges. It can be resumed automatically by a task scheduler just before the ETL jobs begin. Simply reducing size (B) might lower cost per hour but doesn’t address the 20+ idle hours. A single, larger cluster (C) doesn’t solve the idle time problem. Auto-suspend (D) is good for unpredictable workloads, but with a known daily schedule, explicit control is more reliable and ensures the warehouse is ready exactly when needed.

To create objects (tables, views) in a schema, a role needs: 1) USAGE on the parent database, 2) USAGE on the specific schema, and 3) the specific creation privilege (CREATE TABLE, CREATE VIEW, etc.) on that schema. Granting these privileges allows the role to create its own objects and automatically become their owner (with full control over them), without granting the ability to drop the schema or modify objects owned by others. OWNERSHIP (C) is excessive and dangerous. There is no single `CREATE OBJECT` privilege (D); creation privileges are granular (CREATE TABLE, CREATE VIEW, etc.). Option A misses the required USAGE on the schema.

The most efficient method is to use `LATERAL FLATTEN` directly within the data loading statement (like `INSERT INTO … SELECT`). This allows Snowflake to parse the JSON variant data and flatten the nested array in a single step, writing the results directly to the target child table. It’s a set-based operation that leverages Snowflake’s compute. Option B introduces a two-step process. Option C moves processing outside of Snowflake, adding complexity. Option D is similar to B, involving an intermediate view step rather than a direct load.

Spilling to remote storage indicates that the intermediate data produced during query execution (like large joins or sorts) exceeds the available memory of the warehouse. The most effective first step is to optimize the query logic itself to reduce the volume of data being processed in memory. Reviewing the join order and applying filters early can dramatically shrink intermediate results, potentially eliminating the spill. While increasing warehouse size (A) can help, it’s a brute-force solution that increases cost and should be considered after query tuning. Clustering (B) helps with pruning but doesn’t directly address in-memory operations for a single query. Using a CTE (C) is syntactic and doesn’t inherently change the execution plan or data volume.0