When a query containing a WHERE clause is running longer than expected, and the Query Profile shows that all micro-partitions are being scanned, the query can be optimized by adding a clustering key to the table.
Understanding Micro-Partitioning in Snowflake:
Snowflake automatically partitions tables into micro-partitions for efficient storage and query performance.
Each micro-partition contains metadata about the range of values it holds, which helps in pruning irrelevant partitions during query execution.
Role of Clustering Keys:
A clustering key defines how data in a table is organized within micro-partitions.
By specifying a clustering key, you can control the physical layout of data, ensuring that related rows are stored together.
This organization improves query performance by reducing the number of micro-partitions that need to be scanned.
Optimizing Queries with Clustering Keys:
Adding a clustering key based on columns frequently used in WHERE clauses helps Snowflake quickly locate and scan relevant micro-partitions.
This minimizes the amount of data scanned and reduces query execution time.
Example:
ALTER TABLE my_table CLUSTER BY (column1, column2);
This command adds a clustering key tomy_tableusingcolumn1andcolumn2.
Future queries that filter on these columns will benefit from improved performance.
Benefits:
Reduced query execution time: Fewer micro-partitions need to be scanned.
Improved resource utilization: More efficient data retrieval leads to lower compute costs.
Snowflake Documentation: Clustering Keys
Snowflake Documentation: Query Profile

When the COPY INTO command is executed in Snowflake, it processes all files present in the specified stage that have not been ingested before or marked as already loaded. Since the user did not remove the original 10 files after the first load, running the same COPY INTO command again will result in all 20 files being processed. This means that the data from the original 10 files will be appended to the table again, along with the data from the new 10 files, potentially leading to duplicate records for the original data set.
References:
* Snowflake Documentation on Data Loading
* SnowPro Core Certification Study Guide

The COPY INTO command is used in Snowflake to unload data from a table into a file in a stage. This command allows for the export of data from Snowflake tables into flat files, which can then be used for further analysis, processing, or storage in external systems.
References:
* Snowflake Documentation on Unloading Data
* Snowflake SnowPro Core: Copy Into Command to Unload Rows to Files in Named Stage

Snowflake's architecture is divided into three layers: database storage, query processing, and cloud services.
The metadata, which includes information about the structure of the data, the SQL operations performed, and the service-level policies, is stored in the cloud services layer. This layer acts as the brain of the Snowflake environment, managing metadata, query optimization, and transaction coordination.