Databricks Data Engineer Associate Question 384
Single answerYou are tasked with setting up a data pipeline to process files arriving in a cloud storage location. The files arrive at unpredictable intervals, and new files need to be processed automatically without requiring manual intervention. The source directory may contain millions of files, and you want to ensure that only newly arrived files are processed efficiently. Which Databricks feature is most suitable in this scenario?
- A
Use Auto Loader to incrementally and efficiently process newly arrived files.
- B
Use the Databricks COPY INTO command to load all files from the source directory.
- C
Use a scheduled Spark job to scan the source directory and process all files regularly.
- D
Use a manual ETL process to monitor the source directory for new files and load them.
Show answer and explanation
Correct answer: A
Explanation
Auto Loader is the most suitable solution for this scenario because it is specifically designed to incrementally process newly arrived files in cloud storage. It eliminates the need for full directory scans, making it highly efficient for handling large-scale file ingestion with unpredictable arrival times.
- A. Correct.
Auto Loader is designed to handle scenarios where files arrive at unpredictable intervals and ensures efficient incremental processing by tracking new files without scanning the entire directory.
- B. Incorrect.
The COPY INTO command is not optimal for processing new files in a directory with millions of files, as it does not inherently track newly arrived files, leading to potential inefficiencies.
- C. Incorrect.
A scheduled Spark job that scans the directory regularly would be inefficient for large directories with millions of files, as it requires repeatedly scanning all files to determine which ones are new.
- D. Incorrect.
A manual ETL process would not be practical or scalable for handling large volumes of files arriving at unpredictable intervals, as it requires human intervention.