Microsoft Azure Data FundamentalsDescribe core data conceptsHard
A data architect is designing a system to process daily sales reports. The raw sales data arrives throughout the day and needs to be collected, cleaned, transformed, and then loaded into a data warehouse for end-of-day analysis. This process runs once every 24 hours. Which data processing option is most appropriate for this scenario?
- AOnline Transaction Processing (OLTP)
- BOnline Analytical Processing (OLAP)
- CBatch Processing
- DStream Processing
Show answer & explanationAnswer & explanation
Correct answer: C. Batch Processing
Batch processing is ideal for scenarios where data is collected over a period and processed in large chunks at scheduled intervals, such as daily sales reports requiring collection, cleaning, and loading into a data warehouse once every 24 hours.
Why the other options are wrong
- A. OLTP is for transactional systems, not for scheduled data transformation and loading for analysis.
- B. OLAP is for querying and analyzing data, not for the underlying data collection and transformation process itself.
- D. Stream processing is for real-time, continuous data, not for scheduled, end-of-day processing.
Batch Processing
A method of processing data in large groups (batches) at scheduled intervals, rather than in real-time.
- Processes data in chunks
- Scheduled execution
- Suitable for large volumes, non-real-time needs
Memory trick: Batch processing is like baking a 'batch' of cookies once a day.