A retail company has over 3000 stores all using the same Point Of Sale (POS) system. The company wants to deliver near real-time sales results to category managers. The stores operate in a variety of time zones and exhibit a dynamic range of transactions each minute, with some stores having higher sales volumes than others.
Sales results are provided in a uniform fashion using data engineered fields that will be calculated in a complex data pipeline. Calculations include exceptions, aggregations, and scoring using external functions interfaced to scoring algorithms. The source data for aggregations has over 100M rows.
Every minute, the POS sends all sales transactions files to a cloud storage location with a naming convention that includes store numbers and timestamps to identify the set of transactions contained in the files. The files are typically less than 10MB in size.
How can the near real-time results be provided to the category managers? (Choose two.)
- A.All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion.
- B.A Snowpipe should be created and configured with AUTO_INGEST = TRUE. A stream should be created to process INSERTS into a single target table using the stream metadata to inform the store number and timestamps.
- C.A STREAM should be created to accumulate the near real-time data and a TASK should be created that runs at a frequency that matches the real-time analytics needs.
- D.An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs.
- E.The COPY INTO command with a task scheduled to run every second should be used to achieve the near-real time requirement.