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Answer: None of these changes will need to be made
Delta Live Tables (DLT) supports both Python and SQL for defining data transformations, allowing teams to leverage the strengths of both languages within the same pipeline. DLT is designed to handle streaming inputs efficiently, making it fully compatible with pipelines that ingest data in real-time. Furthermore, the medallion architecture (raw, bronze, silver, and gold layers) aligns well with DLT's capabilities for managing and processing data at different stages of refinement. Therefore, migrating to Delta Live Tables does not require abandoning the use of multiple programming languages, switching from a streaming to a batch source, or moving away from the established multi-hop architecture. Delta Live Tables enhances the pipeline with features like automatic error handling, data quality checks, and simplified management, without necessitating fundamental changes to the pipeline's design or operation.
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When transitioning a data pipeline to Delta Live Tables, with Python used for the raw, bronze, and silver layers, and SQL for the gold layer, and a streaming input as the raw source, what adjustment is necessary?
A
The pipeline will need to be written entirely in SQL
B
The pipeline will need to use a batch source in place of a streaming source
C
None of these changes will need to be made
D
The pipeline will need to be written entirely in Python
E
The pipeline will need to stop using the medallion-based multi-hop architecture