@ashap551 

You're essentially implementing a well-optimized micro-batching process, and functionally, it's very similar to what readStream() with Autoloader would do. However, there are some advantages to using Autoloader and a proper streaming table that might be worth considering.

Concrete Advantages of Streaming Tables & Autoloader in Your Case

1. Scalability & Efficiency

   • Your current approach works well because you control partitioning and file listing manually. But as the number of files grows, spark.read.parquet() might experience performance degradation due to listing overhead.

   • Autoloader eliminates the need for explicit listing by leveraging AWS SNS/SQS or its file notification mode, reducing metadata operations.

2. No Need for Explicit Checkpointing & File Filtering

   • Right now, you're manually tracking the last processed file via checkpoints.

   • With readStream(), Autoloader automatically tracks processed files and ensures no duplication without requiring explicit filtering logic.

3. True Streaming vs. Continuous Micro-Batching

   • Even though your workflow runs every ~30 seconds, there's still a small gap where data is waiting to be processed.

   • readStream() with Trigger.Once or Trigger.AvailableNow can reduce end-to-end latency since Spark processes new files immediately instead of waiting for the next scheduled batch.

4. Automatic Schema Evolution

   • You mentioned handling schema evolution manually. Autoloader can simplify this with mergeSchema = true and Databricks' schema evolution capabilities.

5. Easier Integration with Delta Change Data Feed (CDF)

   • If your use case evolves and you need CDF, streaming tables integrate more naturally with readStream() and writeStream().

6. Cost Optimization with Serverless Compute (Future Consideration)

   • Since you’re keeping an interactive cluster always running, this might be expensive. With streaming tables, you could potentially move to Databricks Serverless Streaming or Photon, reducing costs.

Should You Switch?

If your current method is working well, there's no immediate need to switch. However, if you're expecting higher data volumes, schema changes, or lower-latency requirements, then using Autoloader and readStream() would provide more efficiency and automation.