Databricks Data Engineer Associate Question 386
Single answerA data engineering team is tasked with ingesting data from a cloud storage location where new files are continuously added in real-time. The files are semi-structured JSON files, and the data volume is expected to grow significantly over time. The team wants to ensure that they can automatically process newly arriving files without the risk of processing the same file multiple times or missing any files. Which tool or approach would be most beneficial in this scenario?
- A
Use Auto Loader to incrementally and efficiently load new files from the cloud storage location.
- B
Manually list all files in the cloud storage location and use Spark to process them in batches.
- C
Set up a cron job to periodically scan the cloud storage location and process all files using Spark.
- D
Use a traditional ETL pipeline that processes all files in the cloud storage location every day.
Show answer and explanation
Correct answer: A
Explanation
Auto Loader is beneficial in scenarios where new files are continuously added to a data source, such as cloud storage. It provides an efficient, incremental approach to file ingestion, ensuring that each file is processed exactly once without manual intervention or the need for full directory scans. This makes it an ideal tool for handling real-time or near-real-time data ingestion at scale.
- A. Correct.
This is the correct option. Auto Loader is designed for scenarios where new data files are continuously added to a cloud storage location. It can efficiently detect and load new files without reprocessing already processed files.
- B. Incorrect.
This option requires significant manual effort and increases the risk of missing files or reprocessing the same files multiple times.
- C. Incorrect.
A cron job would involve periodic scanning, which can lead to unnecessary reprocessing and inefficiencies. It also doesn’t provide the incremental processing capabilities of Auto Loader.
- D. Incorrect.
A traditional ETL pipeline is not designed for real-time or incremental file ingestion. Processing all files daily is inefficient and may delay data availability.