D: Leveraging Spark‘s mapPartitions to perform batched API requests for each partition.

This is the most efficient and recommended strategy for enriching large-scale raw event data with lookups from an external REST API inside a Spark job.

Why D is the best choice:

• API call efficiency: Row-by-row UDFs (or map()) would generate millions of individual HTTP requests, leading to throttling, high latency, connection overhead, and poor performance. mapPartitions lets you process an entire partition (a batch of rows) at once. Inside the partition iterator, you can:

  • Batch multiple lookup keys into fewer (ideally one) API calls.
  • Reuse HTTP connections/sessions.
  • Optionally add parallelism (e.g., multithreading) within the partition.
  • Handle retries, rate limiting, and errors at the batch level.

• Scalability with Spark's distributed model: Partitions are naturally processed in parallel across executors. This aligns with Spark's execution model without unnecessary data movement.

• Common Databricks/Spark pattern: This is a standard approach for external system enrichment (REST APIs, databases without native connectors, ML inference, etc.). Databricks documentation and community resources frequently highlight it for scalable REST interactions.

Why not the other options?

• A: Storing reference data in Delta Lake + caching is excellent if the reference data is static or can be periodically materialized (e.g., daily refresh). However, the question specifies "from an external REST API," implying dynamic/on-the-fly lookups. Delta caching optimizes joins but doesn't address live API calls.

• B: RDD-based manual management is low-level, error-prone, and bypasses the optimized DataFrame API (Catalyst, Tungsten, etc.). It's rarely the best choice in modern Spark/Databricks workflows.

• C: Broadcast join is ideal for small, static reference data that fits in memory (e.g., a small lookup table fetched once and broadcast). It doesn't directly handle dynamic external API calls per event. If the reference data can be fully fetched upfront, you could load it into a small DataFrame and broadcast, but this isn't the general case for ongoing REST enrichment.

Additional best practices in Databricks:

• Use foreachBatch for streaming scenarios.
• Consider connection pooling and rate limiting inside mapPartitions.
• Monitor with Spark UI (task duration, shuffle, etc.) and handle failures gracefully (e.g., dead-letter queues).
• For very high scale, explore Databricks features like the Python Data Source API or external services (Functions/Lambda) as intermediaries.

This approach balances performance, reliability, and maintainability for production ETL pipelines.