Apache Flink and Apache Spark, as alternatives to Hadoop MapReduce for Big Data analytics, offer more choice and more confusion!
This talk analyzes the similarities and differences between Apache Flink and Apache Spark.
The approach taken is unique as it is based on covering many topics such as computing engine (runtime), APIs, domain specific libraries, architecture, programming models, tools, big data stack, ecosystem, community, …
The main goal is to share with users some knowledge that could help them when selecting Spark and/or Flink.
Developers of both projects might also find this analysis helpful and learn a few things from each other«™s.
About the speaker
Slim Baltagi is director of Big Data engineering at Capital One Financial Corporation in Chicago.
He has more than 17 years of IT and business experience and has spent the last four years of his life hadooping and more recently sparking and flinking! He has worked on more than a dozen Big Data projects as a solution architect. He enjoys evangelizing Big Data technologies by speaking at Big Data events and maintaining a blog and a Knowledge Base on many Apache projects: Hadoop, Spark, Flink.
With some fellow squirrels, Slim also runs the Chicago Apache Flink Meetup and the Washington DC Area Apache Flink Meetup!