Qlik Cloud Data Integration can now transform data ingested by third-party tools and data shared within a data warehouse or lake. 
 
We looked at two sets of data pipeline needs.
 
The first set is the requirements during data ingestion. Users wanted the ability to deselect and not replicate specified fact tables or other tables. They also wanted to eliminate or minimize full reloads when source schema changes or data environments fail.
 
The second set of requirements was during data transformation. Users asked for model-driven data warehouse automation capabilities that accommodate late-arriving dimensions. They wanted to delay merges of real-time data with historical data and align their cloud computing costs with analytics consumption needs. They also wanted to intelligently choose between materialized and non-materialized tables by balancing the number of users expected to access the table or view and real-time expectations of the data to prevent unnecessary data churn.

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