Differentiate between Kafka streams and Spark Streaming.

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Kafka Streams	Spark Streaming
Kafka is fault-tolerant because of partitions and their replicas.	Using Cache and RDD (Resilient Distributed DATASET), Spark can restore partitions.
It is only capable of handling real-time streams	It is capable of handling both real-time and BATCH tasks.
Messages in the Kafka log are persistent.	To keep the data durable, you'll need to utilize a dataframe or ANOTHER data structure.
There are no interactive modes in Kafka. The data from the producer is simply consumed by the broker, who then WAITS for the client to read it.	Interactive modes are available.

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