Adam Kawa became a fan of Big Data after implementing his first Hadoop job in 2010. Since then he has been working with Hadoop at Spotify (where he had proudly operated one of the largest and fastest-growing Hadoop clusters in Europe for two years), Truecaller, Authorized Cloudera Training Partner and finally now at GetInData. He works with technologies like Hadoop, Hive, Spark, Flink, Kafka, HBase and more. He has helped a number of companies ranging from fast-growing startups to global corporations. Adam regularly blogs about Big Data and he also is a frequent speaker at major Big Data conferences and meetups. He is the co-founder of Stockholm HUG and the co-organizer of Warsaw HUG.

Topic: Streaming analytics better than batch – when and why

While a lot of problems can be solved in batch, the stream processing approach currently gives you more benefits. And it’s not only sub-second latency at scale. But mainly possibility … to express accurate analytics with little effort – something that is hard or usually ignored with older batch technologies like Pig, Scalding, Spark or even established stream processors like Storm or Spark Streaming. In this talk we’ll use a real-world example of user session analytics (inspired by Spotify) to give you a use-case driven overview of business and technical problems that modern stream processing technologies like Flink help you solve, and benefits you can get by using them today for processing your data as a stream.