Databricks Data Engineer Associate Question 387
Single answerYou are working on a data engineering project where a large volume of JSON files is continuously being uploaded to a cloud storage location. These files need to be ingested into a Delta Lake table with minimal manual intervention, ensuring that new files are automatically detected and processed. Which Databricks feature would be most beneficial in this scenario?
- A
Using Auto Loader to incrementally process new files as they arrive
- B
Manually triggering batch jobs to process files every hour
- C
Using a traditional ETL tool that requires a fixed schema
- D
Using a Spark job to process all files in the directory each time the job runs
Show answer and explanation
Correct answer: A
Explanation
Auto Loader is beneficial in scenarios where new files are continuously arriving in cloud storage and need to be processed incrementally. It simplifies the ingestion process by automatically detecting new files and supports schema evolution, making it an efficient and scalable solution for streaming data pipelines.
- A. Correct.
Correct. Auto Loader is specifically designed for incrementally and automatically processing new files as they arrive in cloud storage, making it ideal for this scenario.
- B. Incorrect.
Incorrect. Manually triggering batch jobs adds unnecessary overhead and does not provide automation, which is essential in this scenario.
- C. Incorrect.
Incorrect. Traditional ETL tools often require fixed schemas and are not optimized for handling continuously arriving files in cloud storage.
- D. Incorrect.
Incorrect. Processing all files in the directory each time is inefficient and can lead to reprocessing of already ingested data, which is not ideal for this use case.