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An overview of the benefits of Qlik Compose for DWH compared to Microsoft SSIS
The Qlik Data Integration Platform is a complete solution with all necessary functions for DataOps for Analytics. Attunity technology is the core of Qlik's advanced data integration capabilities.
To the Qlik Data Integration Platform
Classic ETL approach for Analytics Architecture / DWH data flow requires a clearly defined requirements profile in advance, so that the requirements engineering effort is high.
Especially when combining several different systems, such as ERP, PLM, CRM etc., a lot of data mapping takes place, which generates additional transformation effort.
There is little room for agility in the data model or data mart structure, as many adaptations would require changes to the entire DWH and DB structure.
Example: A Finance Data Mart would access data from 2-3 different source systems and many of the ETL sets created for these systems would be affected by adjustments.
Changes in the data structure (DDL) of the source systems, e.g. by adapting the application (also in the future - not only during implementation) result in the fact that many ETL routes have to be adapted (mostly manually) -> high follow-up costs.
Adding new data sources / data sets or integrating new requirements often resembles a whole project cycle and generates a lot of waiting time in the business.
This rather rigid data management is based on "scheduled jobs" (scheduled scripts that trigger data loading routes / transformation routes), which often run once per night - 3 challenges:
Often it is only noticed the next day that certain system pulls / loading cycles were not successfully performed, which leads to analysis problems / failures.
High monitoring effort, as errors can occur in many different ways within these often complex data loading processes.
No possibility to actively obtain data for time-critical business cases faster or to work with even more current data.
Not only in the implementation but also in the operation of the DWH / architecture SSIS / the ETL approach offers only limited possibilities to manage, maintain and further develop the data model end-to-end via a central user friendly interface in order to keep data marts "always-up-to-date".