Databricks Data Engineer Associate Question 83
Select 3A data engineering team is tasked with building an ELT pipeline using Apache Spark on Databricks. The pipeline reads raw JSON data from a cloud storage location, transforms it into a structured Delta table, and then loads the table into a presentation layer for analytics. Which of the following steps should the team include in the pipeline to ensure optimal performance and maintainability?
- A
Use Spark's built-in schema inference to automatically infer the schema of the JSON data.
- B
Persist intermediate transformed data as Delta tables to support incremental processing and ACID compliance.
- C
Repartition the data based on the most commonly queried columns before writing it to the Delta table.
- D
Use the 'overwrite' mode when loading the presentation layer to ensure the latest data replaces the existing data.
- E
Apply column pruning and predicate pushdown during transformations to minimize data shuffling.
Show answer and explanation
Correct answers: B, C, E
Explanation
Building an ELT pipeline with Apache Spark involves implementing best practices to optimize performance and ensure maintainability. Persisting intermediate data as Delta tables provides reliability and incremental processing capabilities. Repartitioning data based on query patterns reduces shuffling and improves performance, while applying column pruning and predicate pushdown minimizes unnecessary data movement. These steps are critical for building efficient and scalable pipelines.
- A. Incorrect.
While Spark's schema inference can simplify development, it is not recommended for production pipelines because it can lead to inconsistent schemas if the input data changes. Explicitly defining the schema is a better practice.
- B. Correct.
Persisting intermediate data as Delta tables is a best practice for ELT pipelines. It ensures data durability, supports incremental processing, and provides ACID compliance for reliable transformations.
- C. Correct.
Repartitioning data based on commonly queried columns optimizes data layout, reducing the overhead of shuffling and improving query performance.
- D. Incorrect.
Using the 'overwrite' mode without additional safeguards can inadvertently cause data loss if not used carefully. A more robust approach is to use merge or upsert operations in the presentation layer.
- E. Correct.
Applying column pruning and predicate pushdown improves performance by reducing the amount of data read and processed during transformations, minimizing shuffle operations.