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Answer: All of the above.
When designing the data ingestion layer for a data analytics project, it is important to follow best practices. Using Azure Data Factory or Azure Databricks for data ingestion (Option A) allows you to leverage their built-in capabilities and connectors, simplifying the integration process. Implementing data validation and cleansing processes (Option B) ensures data quality and accuracy, which is crucial for reliable analytics. Designing the data ingestion layer to be scalable (Option C) allows it to handle increasing data volumes and sources, ensuring long-term sustainability. Therefore, all of these best practices should be followed, making Option D the correct answer.
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You are working on a data analytics project that requires the integration of data from multiple sources into Azure Synapse Analytics. What are some of the best practices to follow when designing the data ingestion layer?
A
Use Azure Data Factory or Azure Databricks for data ingestion to leverage their built-in capabilities and connectors.
B
Implement data validation and cleansing processes to ensure data quality and accuracy.
C
Design the data ingestion layer to be scalable and able to handle increasing data volumes and sources.
D
All of the above.
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