An Architect needs to improve the performance of reports that pull data from multiple Snowflake tables, join, and then aggregate the data. Users access the reports using several dashboards. There are performance issues on Monday mornings between 9:00am-11:00am when many users check the sales reports.
The size of the group has increased from 4 to 8 users. Waiting times to refresh the dashboards has increased significantly. Currently this workload is being served by a virtual warehouse with the following parameters:
AUTO-RESUME = TRUE AUTO_SUSPEND = 60 SIZE = Medium
What is the MOST cost-effective way to increase the availability of the reports?
A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.
The data pipeline needs to run continuously ang efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.
Which design will meet these requirements?
By executing the 'SHOW TABLES' command, we can list all the tables in all the schemas even if we do not have access to all the tables
The following chart represents the performance of a virtual warehouse over time:
A Data Engineer notices that the warehouse is queueing queries. The warehouse is sizeX-Small, theminimum and maximum cluster counts are set to 1, thescaling policy is set to standard, andauto-suspend is set to 10 minutes.
How can the performance be improved?
While creating a clustering key, what is the recommendation for maximum number of columns that you can include as part of the key?
