Which data processing framework is commonly used to ingest data onto cloud data platforms in Azure?

The Extract, Load, and Transform (ELT) framework is commonly used to ingest data onto cloud data platforms in Azure because it aligns well with the cloud architecture and data processing capabilities of Azure services. In ELT, raw data is first extracted from various sources and then loaded into a data storage solution before any transformations are applied. This approach takes advantage of the scalability of cloud environments, allowing for large volumes of data to be stored quickly and transformed only when needed for analysis.

In cloud platforms like Azure, the architecture often supports the storage of large datasets, which can be transformed later as required rather than during the ingestion process. Services such as Azure Data Lake Storage or Azure Synapse Analytics facilitate this method effectively, enabling efficient handling of data for analytics and reporting. With the rise of data lakes and the necessity for handling diverse data types, ELT has become a preferred paradigm for modern data workflows in cloud environments. This methodology leads to greater flexibility and better performance, especially when dealing with big data scenarios.

In contrast, OLTP is focused on transaction processing rather than data ingestion, while traditional ETL typically involves transforming the data before loading, which may not be optimal for large-scale cloud environments. Batch processing is a method for processing data in large blocks