Efficient and scalable transformation in the cloud, regardless of how or with what tool the data was ingested, is crucial to creating the agility and flexibility to mix and match data for any number of use cases or downstream applications, real time and embedded analytics or generative AI and machine learning efforts.
These are the key drivers behind our recent launch of a new set of Qlik Cloud® data transformation services, helping customers drive more value from data by simplifying and automating transformation of data from any third-party source directly in the cloud.
Qlik’s new Transformation Services through Qlik Cloud Data Integration helps customers automate and scale the process of transforming these large volumes of ingested data directly in the cloud where it resides. With Qlik’s Transformation Services:
- Customers can execute transformations directly in the cloud where the data resides, be it Databricks Lakehouse, Google Cloud, Microsoft Azure, Snowflake or Qlik’s own cloud, reduces risk by executing the transformation in one place.
- Any 3rd party ingested data can be transformed by Qlik Cloud, even if it is ingested by solutions such as Fivetran or HVR, into a data mart or any existing data architecture.
- By executing the transformations in one place, it makes it easier for customers maintain history as part of their broader transformation processes, whether it ELT or ETL.
- And for customers who already use Qlik Data Integration for data movement, being able to leverage one combined solution for movement and transformation saves time and cost, while also reducing risk.
- Ultimately, Qlik’s Transformation services ensures the data in any cloud is available for use and action across the business.
This video shows these amazing capabilities in action.
Without the ability to efficiently transform data at scale, customers are not able to get the maximum value of all their data in the cloud, regardless of which cloud they choose to use. Qlik Cloud Data Integration’s new Transformation Services helps customer be more agile and flexible by automating the transformation of data from any source, making more data in any cloud ready and available for the entire range of downstream applications, analytics and generative AI efforts.