Databricks Certified Data Engineer - Associate

Explanation

Auto Loader uses Spark Structured Streaming to process data incrementally. Here's why:

Key Points:

1. Auto Loader Architecture: Auto Loader is built on top of Spark Structured Streaming, which provides the incremental processing capabilities.

2. How Auto Loader Works:

   • Auto Loader monitors cloud storage locations for new files
   • It uses Structured Streaming's micro-batch processing to incrementally load new data
   • The process tracks which files have been processed using checkpointing

3. Checkpointing Role: While checkpointing (Option A) is used by Auto Loader to track processed files and maintain state, it's not the primary tool for incremental processing. Checkpointing is a supporting mechanism.

4. Data Explorer: This is a Databricks tool for exploring and visualizing data, not related to incremental data processing.

Correct Answer: B. Spark Structured Streaming - This is the underlying engine that enables Auto Loader's incremental processing capabilities.

Additional Context:

• Auto Loader provides optimized file discovery and schema inference on top of Structured Streaming
• It's designed specifically for incremental data ingestion from cloud storage
• The incremental processing happens through Structured Streaming's micro-batch architecture