Snowflake a great data warehousing tool.
Use Cases and Deployment Scope
We work as a team setting up the Snowflake environment for our clients, which includes setting up production, development, and testing environments, setting up the role-based access control, and implementing masking policies. We create pipelines using tools like Azure Data Factory, airflow, and Matillion to bring clients' raw data into a Snowflake. Then, we create procedures and tasks on top of them to clean that data and transform the data for reporting purposes, and then we use the Snowflake consumption layer for all our reporting purposes and create reports out of it. We also use Snowsight for some of the Snowflake usage reports, such as cost monitoring and query monitoring. We have also made use of the dynamic table tables where we had a requirement to refresh the tables on an hourly basis so that we don't have to create multiple elements like task stream. The dynamic table can take care of everything.
Pros
- Creating Procedures.
- Python integration.
- Snowsight for reports.
- Data masking.
- RBAC
Cons
- Subquery.
Likelihood to Recommend
Snowflake is well suited when you have to store your data and you want easy scalability and increase or decrease the storage per your requirement. You can also control the computing cost, and if your computing cost is less than or equal to 10% of your storage cost, then you don't have to pay for computing, which makes it cost-effective as well.
