Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 4 months ago

Spark Streaming: The component for real-time data processing and analytics. - Source: dev.to / over 1 year ago

Is a big data framework and currently one of the most popular tools for big data analytics. It contains libraries for data analysis, machine learning, graph analysis and streaming live data. In general Spark is faster than Hadoop, as it does not write intermediate results to disk. It is not a data storage system. We can use Spark on top of HDFS or read data from other sources like Amazon S3. It is the designed... - Source: dev.to / over 2 years ago

You might want to try: https://github.com/lovoo/goka -- it uses levelDB to keep state from a stream. The application we wrote in-house with goka can process (keeping state) approximately 800+ messages/sec per consumer in a consumer-group. Source: about 3 years ago