For Azure Stream Analytics, which output type is best for archiving event data?

The best output type for archiving event data in Azure Stream Analytics is Blob storage or Azure Data Lake Storage Gen2 (ADLS Gen2). These storage solutions are designed to handle large volumes of unstructured data, making them ideal for archiving purposes.

Blob storage allows for scalable storage of both structured and unstructured data, and it supports different data formats, including JSON and binary files. This feature is essential for archival purposes, as it enables you to store vast amounts of historical data efficiently.

ADLS Gen2 builds upon Blob storage and adds hierarchical file system capabilities, which provide better management and performance for analytics workloads. It is optimized for big data analytics and offers high throughput and low latency, which can be beneficial when dealing with large datasets for archiving.

In contrast, using a compressed JSON file may not be practical for long-term storage due to its lack of structure and the overhead associated with handling compressed files. Azure SQL Database is typically used for transactional workloads and online analytics but may not scale efficiently for large volumes of archive data. Azure Cosmos DB, while excellent for high availability and low-latency access to data with multi-model support, is often more expensive compared to Blob storage for pure archival use and not specifically designed for large-scale event