Databricks Data Engineer Associate Question 219
Select 2A data engineering team is tasked with processing real-time streaming data from a Kafka topic into a Delta table using Databricks. They want to ensure that only new records are processed and no duplicates are written to the Delta table. Which of the following configurations should they use to achieve incremental processing and deduplication?
- A
Set the 'checkpointLocation' option in the streaming query.
- B
Use the 'merge' operation in conjunction with a unique key to handle duplicates.
- C
Use the 'append' output mode for the streaming query.
- D
Enable the 'ignoreDeletes' option in the Delta table configuration.
- E
Set the 'trigger' option to 'once' for batch-style incremental processing.
Show answer and explanation
Correct answers: A, B
Explanation
To achieve incremental processing with deduplication in Databricks, it is crucial to configure checkpointing to track the state of the streaming query and use a 'merge' operation with a unique key to ensure only new or updated records are written to the Delta table. This combination allows efficient handling of real-time streaming data while avoiding duplication.
- A. Correct.
The 'checkpointLocation' option ensures that the state of the streaming query is tracked, allowing incremental processing by keeping track of processed data.
- B. Correct.
The 'merge' operation with a unique key enables deduplication by ensuring that only new or updated records are written to the Delta table.
- C. Incorrect.
The 'append' output mode does not handle deduplication as it simply appends all incoming data to the Delta table.
- D. Incorrect.
The 'ignoreDeletes' option is unrelated to incremental processing and deduplication; it is used for handling delete events in change data capture (CDC) scenarios.
- E. Incorrect.
Setting the 'trigger' option to 'once' enables batch-style incremental processing, but it does not inherently handle deduplication.