Contributing to Open Source Big Data Tech

There are 6 projects that I have on my list of Open Source Technologies in the Big Data space that I want to contribute to :

(in no particular order)

• Spark
• Kafka
• Beam
• Flink
• Mahout
• Streams

 

Understanding Kafka Streaming using the WordCountDemo

I have been spending some time this weekend trying to understand Kafka.

Kafka has four core APIs:

• The Producer API :
  • allows an application to publish a stream records to one or more Kafka topics.
• The Consumer API
  • allows an application to subscribe to one or more topics and process the stream of records produced to them.
  • the Producer + Consumer API’s are essentially achieving the functionality provided by Azure’s EventHub
• The Streams API :
  • allows an application to act as a stream processor, consuming an input stream from one or more topics and producing an output stream to one or more output topics, effectively transforming the input streams to output streams.
  • the Streams API is achieving what Azure’s ASA does.
• The Connector API
  • allows building and running reusable producers or consumers that connect Kafka topics to existing applications or data systems. For example, a connector to a relational database might capture every change to ? 🙂
  • the Connector API is achieving what Azure’s ADF does.

 

The documentation uses the WordCountDemo as the motivating example for the Streaming scenario. So I decided to spend some time over the weekend understanding this API better.

References:

• http://kafka.apache.org/documentation.html
• http://docs.confluent.io/3.0.0/streams/quickstart.html

Code: