NoLambda: A new architecture combining streaming, ad hoc, machine learning, and batch analytics

Location: Salon A
April 11th, 2016
1:30 PM - 2:30 PM

In today’s world of exploding big and fast data, developers who want both streaming analytics and ad hoc, OLAP-like analysis have often had to develop complex architectures such as Lambda—a path for fast streaming analytics using NoSQL stores such as Cassandra and HBase with a separate batch path involving HDFS and Parquet. While this approach works, it involves too many moving parts, too many technologies for ops, and too many engineering hours. Helena Edelson and Evan Chan highlight a much simpler approach to combine streaming and ad hoc/batch analysis using what they call the NoLambda stack (Apache Spark/Scala, Mesos, Akka,

Evan Chan

Creator, FiloDB

Demystifying Stream Processing with Apache Kafka

Location: Salon A
April 12th, 2016
11:30 AM - 12:30 PM

The concept of stream processing has been around for a while and most software systems operate as simple stream processors at their core: they read data in, process it, and maybe emit some data out. So why are there so many stream processing frameworks, all with different terminology, and why does it seem so complex to get up and running? What benefits does each stream processing system provide, and more importantly, what are they missing? This presentation will start by abstracting away the individual frameworks and describe the key features and benefits that stream processing frameworks provide. These core features

Ewen Cheslack-Postava

Engineer, Confluent