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ETL Testing for the Data Warehouse

When you are moving or developing a data warehouse from scratch, it's inevitable that testing will factor into those plans. And  Extract, Transform, and Load (ETL) will be first on the list. ETL is the most common process used to load data from source systems into a data warehouse.

ETL testing is necessary to ensure that data moving from external sources to the data warehouse is accurate at each point between the source and destination. But what if you could reduce -- or even eliminate -- the impact of ETL on internal resources and time to analysis?

Instant Data Integration, No ETL Testing Needed

Say goodbye to ETL and ETL testing headaches. With Snowflake, you can load semi-structured data direct into a relational table, query it with a SQL statement and then join it to other structured data. Snowflake keeps track of the self-describing schema for you. 

Snowflake also offers Partner Connect, which allows you to receive data insights faster. Partner Connect speeds time-to-value with pre-built integrations to ETL and Extract Load Transform (ELT) data integration partners, including Fivetran, Alooma, Stitch, and Matillion ETL. The rapid provisioning of partner applications and automatic connection to Snowflake enable customers to begin loading data into Snowflake within minutes.

For data loading in general, Snowflake offers different load options and copy parameters with different latency characteristics, so you can select an approach based on specific analytic windows and requirements for retaining historical data.

Sign up for the Snowflake Free Trial today. With no commitment, you can load your own data, run sub-second queries and try out your favorite BI tools with full platform access.