To get an idea of how a stream processor can handle batch processing as well, visit http://data-artisans.com/batch-is-a-special-case-of-streaming.

There is a lot of information at the Flink blog on the specific mechanisms that Flink uses to optimize batch processing. If you’d like to dig deep into this, we recommend the following:

• http://flink.apache.org/news/2015/05/11/Juggling-with-Bits-and-Bytes.html

• http://flink.apache.org/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html

• http://data-artisans.com/computing-recommendations-at-extreme-scale-with-apache-flink/

  
  

Companies that are using Flink on a regular basis publish articles on what they achieve with the system and how they are using it. Below is a small selection of links of such user stories:

• https://techblog.king.com/rbea-scalable-real-time-analytics-king/

• https://tech.zalando.de/blog/apache-showdown-flink-vs.-spark/

• http://data-artisans.com/flink-at-bouygues-html/

• http://data-artisans.com/how-we-selected-apache-flink-at-otto-group/

  
  

The Flink Forward conference series publishes most videos and slides of its talks online, which is a great resource to learn more about what companies are doing with Flink:

• Flink Forward 2015: http://2015.flink-forward.org/

• Flink Forward 2016: http://2016.flink-forward.org/

  
  

To learn more about the message-transport technology that is an integral part of the MapR Converged Data Platform, see the following resources:

• For an overview of MapR Streams’ capabilities, including management at the stream level and geo-distributed stream replication, go to https://www.mapr.com/products/mapr-streams.

• For sample programs with MapR Streams (which uses the Kafka API), see “Getting Started with MapR Streams”: https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams.

• For a brief comparison of transport options, see “Apache Kafka and MapR Streams: Terms, Techniques and New Designs”: https://www.mapr.com/blog/apache-kafka-and-mapr-streams-terms-