Qlik announces the ability to transform data ingested by third parties or data-sharing agreements.

[Anand_Rao]

Qlik Cloud Data Integration can now transform data ingested by third-party tools and data shared within a data warehouse or data lake. Transforming data in the cloud enables Qlik customers to leverage automated transformations and pushdown processing to deliver business value while reducing data ingestion costs.

[Anand_Rao]

Qlik identifies current ingested data using their "watermark pattern" to support an incremental loading pattern for data from third-party tools or data-sharing agreements. Recognizing patterns makes the transformations that manage history efficient, allowing Qlik to integrate with existing data ingestion tools.

Data ingestion tools cost more if they track history. Hence, data engineering teams seek to reduce data ingestion costs by transforming ingested data and maintaining records as part of broader transformation capabilities resulting in reduced overall costs.

For existing Qlik customers, since Qlik Cloud Data Integration understands how Qlik Replicate delivers data, users can register, transform, and maintain the history of incrementally ingested data while creating a layer of real-time, live views over the data to reduce cloud computing expenses. Once users register the data into their pipelines, the complete transformation capabilities are available. Hence data engineering teams already having Qlik Replicate can reuse existing artifacts while transitioning to Qlik Cloud Data Integration.

Current Qlik Replicate customers expect to automate their transformations in their cloud data warehouses or lakes without managing on-premises software such as Qlik Compose. Their use cases range from custom SQL-driven output to operational data stores and data warehouses that follow patterns such as Kimball star schema, Inmon, data vault, and multi-zoned lakehouses. 

Contact us to try the new capabilities in the latest release of Qlik Cloud Data Integration to automate data transformations and data marts via the Transform and Data Mart tasks.