Some of the best features of Snowflake Analytics is how it is separated for compute and storage, which allows multiple teams to run queries without performance impact. Our project has more than nine or ten teams, so if a team is working on a particular dashboard and another team wants to work on Snowflake Analytics, they can easily work on it without disturbing the work of one team, ensuring that the work can be consistent and multiple users can use the tool simultaneously. This is one of the main features I appreciate about Snowflake Analytics.The other aspect is how the query performance is high due to its columnar storage and also automatic optimization, allowing for scaling down during weekdays when there is less data and scaling up during weekends or festivals when the data can be huge. Because of this automatic scaling, if there is less data, we are using only 5 GB instead of 10 GB, which reduces the storage level, and consequently, the cost can be reduced. This automatic scaling and optimization saves us a huge amount of money for our project. Snowflake Analytics has impacted our organization positively by providing faster query performance and better scalability. Previously, we faced issues with slow queries due to traditional systems, but within Snowflake, we can assign separate virtual warehouses for reporting as well as data processing, ensuring that it does not impact tool performance and does not delay reporting to business users. Analysts can run complex queries without affecting ETL workloads due to this separation, which has improved our speed of reporting, and our business users have expressed happiness due to how fast we are generating reports during peak hours.
