@MarlinGomez For that CCA175 streaming scenario with inconsistent formats, cleansing/transforming to HDFS, better to go with Spark Structured Streaming + schema evolution as the most exam-realistic pick. It handles real-time ingestion efficiently via micro-batches, infers/evolves schemas on the fly (especially with JSON/Avro), and lets you apply transformations like filter/map before writing Parquet to HDFS. Separate ETL pipelines per format add too much complexity/overhead for exam constraints, and pure schema-on-read skips proactive cleansing. ​QuickStart with Kafka source, schema merging enabled: .option("mergeSchema", "true").writeStream... to HDFS.This nails the "perform ETL on data using Spark API" objective perfectly. Good luck on your prep. ​ ... View more