Architecture of Spark Streaming

Now that we have seen Spark Streaming in action, let's take a step back and try to understand what a stream processing engine should do. On a high level, a distributed stream processing engine uses the following execution model:

1. Receive data from other data sources: This could be web server logs, credit card transactions, Twitter sources, other sensor devices, and so on. Some of the most popular ingestion systems include Apache Kafka, Amazon Kinesis, and so on.
2. Apply business logic: Once the data is received, any distributed stream processing engine will apply the business rules (in a distributed manner). This can include filtering logs, aggregating information, checking for potential fraud, and identifying potential marketing offers. The list is endless, but this is perhaps the reason why you build a streaming application and reduce your reaction time to events of interest.
3. Once you have applied your business rules: You would potentially want to store the results...