Loading data is greatly simplified because complex ETL data pipelines are no longer needed to prepare data for loading. Snowflake supports and optimizes diverse data, both structured and semi-structured, while making that data accessible via SQL. Here, you can learn more about loading data in Snowflake.
Prepare Data for Analysis
One of the biggest advantages of Snowflake is the elastic and scalable compute that allows for ease of analytical data preparation. For example, instead of creating a table and an ETL process to do aggregate calculations or logic, you can create a view on the raw transaction detail. You can then connect tools like Tableau, Power BI, and MicroStrategy to Snowflake using ODBC connectivity, and build analytical reporting on top of your created view. When the report is executed, Snowflake’s virtual warehouse processes the logic on the base tables, and returns the output usually faster than an on-premise traditional data warehouses.
In this approach, we can reduce the number of tables at different aggregate levels and dimensions, while writing and maintaining ETLS to process raw data and tables. We can rely on Snowflake to do all the ETL processing using its massively parallel processing power very quickly. We hope this exercise has helped you get up and running with Snowflake, and realize some of the key benefits integrated within.