Databricks Certified Data Engineer - Associate
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When dealing with large volumes of data in a modern data engineering environment, one effective method for processing this data incrementally is through the use of specialized tools. With that context in mind, consider the following question: Which tool does Auto Loader utilize to handle and process data in incremental steps?
When dealing with large volumes of data in a modern data engineering environment, one effective method for processing this data incrementally is through the use of specialized tools. With that context in mind, consider the following question: Which tool does Auto Loader utilize to handle and process data in incremental steps?
Explanation:
Auto Loader in Databricks uses Spark Structured Streaming to process data incrementally. Spark Structured Streaming is a real-time data processing framework that allows for the incremental processing of data streams as new data arrives. Auto Loader works with Structured Streaming to automatically detect and process new data files added to a specified data source location, ensuring timely and efficient data processing. While checkpointing is a technique used for fault tolerance and exactly-once semantics, it is a part of Structured Streaming, not a tool on its own for processing data incrementally.