Fast and Easy Stream Processing

In July, Hazelcast returns to discuss the various techniques to do stream processing. You can find more about their open source project at https://jet.hazelcast.org/.

SCHEDULE:
6:30 Networking
7:00 Presentation begins
8:30 JetBrains product raffle, then off-site networking

Immediately after the meeting, we will head to El Torito Grill (map: https://goo.gl/maps/x7Sug ) for dinner, drinks, and more networking and discussion.

See you there!

ABSTRACT
Join this meetup and learn about core techniques in stream processing and how to get started building a stream processing application. We will be showcasing real world use cases and demos.

You will learn all about directed acyclic graph (DAG) and why it's so powerful for Big Data processing. We will walk you through the evolution of Big Data computing, from sequential to DAG, as well as other techniques such as Single-Producer/Single-Consumer Queues, Cooperative Multithreading, Data Locality, In-Memory sources and sinks, and WaitFree algorithms that power Big Data processing.

This talk will also feature an introduction to Hazelcast JET, an open source, DAG-based in-memory real time streaming and batch processing engine. With Hazelcast Jet, you can use data stores such as HDFS, Kafka, Hazelcast In-Memory Data Grid and more. We will also review the major differences between Hazelcast Jet, Spark, Twitter Heron, Flink, and Kafka Streams.

We will walk you through writing a sample application and show how you can be up and running in less than a hundred lines of Java code. Demo applications will feature Twitter Cryptocurrency Sentiment Analysis and real-time worldwide commercial aircraft monitoring.

BIO
Gokhan Oner is a Software Developer and Solutions Architect at Hazelcast.

Gokhan has 12+ years of experience, mostly in finance-technology, from server/database management to designing & developing regulatory compliant payment solutions, fraud detection systems and managing data-integration & migration projects. He holds a BS degree in Computer Engineering.