When integrating heterogeneous data sources, organizations often encounter three common challenges:

$1.$ Data Mapping and Transformation: Different data sources may use varying formats, structures, and naming conventions. Mapping and transforming data to a common schema can be complex and time$-$consuming.

$2.$ Data Quality and Consistency: Ensuring consistent data quality across diverse sources is crucial. Inaccurate, incomplete, or inconsistent data can lead to errors and unreliable insights.

$3.$ Performance and Scalability: Integrating large volumes of data efficiently while maintaining system performance can be challenging. Balancing real$-$time processing with scalability is essential.

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