You have an inventory table. You created two views on this table. The views look like as below
CREATE VIEW NON_SECURE_INVENTORY AS
SELECT BIBNUMBER, TITLE, AUTHOR,ISBN
FROM INVENTORY
WHERE BIBNUMBER IN(511784,511805,511988,512044,512052,512063);
CREATE SECURE VIEW SECURE_INVENTORY AS
SELECT BIBNUMBER, TITLE, AUTHOR,ISBN
FROM INVENTORY
WHERE BIBNUMBER IN(511784,511805,511988,512044,512052,512063);
You ran the below queries
ALTER SESSION SET USE_CACHED_RESULT=FALSE;--This is to ensure that we do not retrieve from query cache
SELECT * FROM NON_SECURE_INVENTORY WHERE BIBNUMBER =511784; SELECT * FROM SECURE_INVENTORY WHERE BIBNUMBER =511784;
The query profile for the first query looks as below
However, the query profile for the second one looks like as below
Both the views use the same columns from the same underlying view. So, why is this difference in query profiles.
The diagram shows the process flow for Snowpipe auto-ingest with Amazon Simple Notification Service (SNS) with the following steps:
Step 1: Data files are loaded in a stage.
Step 2: An Amazon S3 event notification, published by SNS, informs Snowpipe - by way of Amazon Simple Queue Service (SQS) - that files are ready to load. Snowpipe copies the files into a queue.
Step 3: A Snowflake-provided virtual warehouse loads data from the queued files into the target table based on parameters defined in the specified pipe.
If an AWS Administrator accidentally deletes the SQS subscription to the SNS topic in Step 2, what will happen to the pipe that references the topic to receive event messages from Amazon S3?
The following DDL command was used to create a task based on a stream:
Assuming MY_WH is set to auto_suspend - 60 and used exclusively for this task, which statement is true?
How can an Architect enable optimal clustering to enhance performance for different access paths on a given table?
A table contains five columns and it has millions of records. The cardinality distribution of the columns is shown below:
Column C4 and C5 are mostly used by SELECT queries in the GROUP BY and ORDER BY clauses. Whereas columns C1, C2 and C3 are heavily used in filter and join conditions of SELECT queries.
The Architect must design a clustering key for this table to improve the query performance.
Based on Snowflake recommendations, how should the clustering key columns be ordered while defining the multi-column clustering key?
