I have been trying to find materials online - both are micro-batch based - so what's the difference ?
Until Spark 2.2, the DStream[T] was the abstract data type for streaming data which can be viewed as RDD[RDD[T]].From Spark 2.2 onwards, the DataSet is a abstraction on DataFrame that embodies both the batch (cold) as well as streaming data.
From the docs
Discretized Streams (DStreams) Discretized Stream or DStream is the basic abstraction provided by Spark Streaming. It represents a continuous stream of data, either the input data stream received from source, or the processed data stream generated by transforming the input stream. Internally, a DStream is represented by a continuous series of RDDs, which is Spark’s abstraction of an immutable, distributed dataset (see Spark Programming Guide for more details). Each RDD in a DStream contains data from a certain interval, as shown in the following figure.
API using Datasets and DataFrames Since Spark 2.0, DataFrames and Datasets can represent static, bounded data, as well as streaming, unbounded data. Similar to static Datasets/DataFrames, you can use the common entry point SparkSession (Scala/Java/Python/R docs) to create streaming DataFrames/Datasets from streaming sources, and apply the same operations on them as static DataFrames/Datasets. If you are not familiar with Datasets/DataFrames, you are strongly advised to familiarize yourself with them using the DataFrame/Dataset Programming Guide.