Optimize your approach to data transformation and use tools that allow you to maintain a single plane of glass into all your data transformation queries to optimize them effectively.
The shift to cloud analytics and cloud data warehouses was supposed to simplify and modernize the data stack for analytics. On-premises, your data stack was simple – an ETL tool such as Informatica and a data warehouse such as Teradata. Yet, many cloud journeys have done quite the opposite – the data stack has gotten more complex and expensive. In the end, this drove up data engineering costs.
The T, or transformation part, is where the raw data loaded into Snowflake is transformed into a form that is useful for analytics and can be directly consumed by analytics and BI tools.
Primary means by which you scale out your data and analytics in Snowflake.
How complex are the queries/models? How much data do the queries/models consume? How are the queries executed?
Modern, scalable cloud data warehouse - that combines to provide a highly scalable and flexible environment to transform your data into meaningful analytics.
Auto-tuning and optimization directly apply to data transformation in Snowflake. In the past, data modelers would define the final queryable structures in a data warehouse for optimal performance using technical attributes. ETL developers would work hand-in-hand to optimize how data was transformed and loaded into the highly tuned data structures in the data warehouse. Even small mistakes could be very costly.