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A Reader account in Snowflake can be used to share data with a consumer who does not have a Snowflake account. Reader accounts are a type of shared account provided by data providers to external data consumers, allowing them to access and query shared data using Snowflake's web interface without needing their own Snowflake account.

References:

• Snowflake Documentation: Reader Accounts

Storing non-native values, such as dates and timestamps, in a VARIANT column in Snowflake can lead to slower query performance and increased storage consumption. VARIANT is a semi-structured data type that allows storing JSON, AVRO, ORC, Parquet, or XML data in a single column. When non-native data types are stored as VARIANT, Snowflake must perform implicit conversion to process these values, which can slow down query execution. Additionally, because the VARIANT data type is designed to accommodate a wide variety of data formats, it often requires more storage space compared to storing data in native, strongly-typed columns that are optimized for specific data types.

The performance impact arises from the need to parse and interpret the semi-structured data on the fly during query execution, as opposed to directly accessing and operating on optimally stored data in its native format. Furthermore, the increased storage consumption is a result of the overhead associated with storing data in a format that is less space-efficient than the native formats optimized for specific types of data.

References:

• Snowflake Documentation on Semi-Structured Data: Semi-Structured Data

The primary factor that determines the cost of using a virtual warehouse in Snowflake is D. The length of time the compute resources in each cluster run. Snowflake's pricing model for compute usage is based on the concept of Snowflake credits, which are consumed based on the time virtual warehouses are running and the size of the warehouses. The more compute resources are utilized and the longer they run, the more credits are consumed, which directly impacts the cost.

References:

• Snowflake Documentation on Virtual Warehouses: Understanding and Managing Warehouse Credit Usage

In Snowflake, auto-scaling allows virtual warehouses to automatically start additional clusters to handle increasing query loads. The setting that triggers the immediate start of additional clusters when a warehouse is running in auto-scale mode is:A. MAX_CLUSTER_COUNT is increased and new_max_clusters is greater than running_clusters: When the maximum number of clusters (MAX_CLUSTER_COUNT) is increased and the new maximum is higher than the number of clusters currently running (running_clusters), additional clusters will start immediately if required by the workload. This configuration ensures that performance scales with demand by allowing more compute resources to be provisioned as needed.

This behavior is designed to maintain performance by dynamically adjusting the compute resources without manual intervention, ensuring that queries are executed with minimal delay, even under varying workloads. It aligns with the principles of elasticity and scalability in cloud computing, particularly within Snowflake's architecture.