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Asked by Twinkle Last Modified
Answer 
This DataStage will continue to see evolutionary changes as the incubator seeks to satisfy the needs of the contemporary data landscape, especially in relation to cloud integration and AI readiness. Here is a simplified version:
* Modernization:
* IBM is working on modernizing DataStage; making it flexible to cloud environments and improving support for ingesting different types of data.
* This includes enhancing the ability to do data integration for AI or BI purposes.
* Cloud integration:
* A prime focus has been to further enhance connectivity with major cloud services like Snowflake, Redshift, Databricks, and Google BigQuery, allowing data to flow more freely between on-premises and cloud systems.
* Adaptability:
* DataStage is adapting to being able to deal with the ever-growing volume and complexity of data, thus ensuring that it would remain relevant for large-scale data processing.
* Challenges:
* DataStage, among others, has been challenged by the more recent cloud-native ETL tools despite its modernization.
* Integration building has become appallingly complex, not only for DataStage but even for more modern integration tools that connect with some bleeding-edge cloud platforms.
In essence, DataStage will have a future adapted to cloud technologies and changing data needs while, at the same time, facing competition from newer integration solutions.
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