Fast Data Streaming in Java

Stream processing has become critical for processing live data in uses like fraud detection, business insights, and more. Selecting the appropriate data streaming architecture is vital, and this talk will provide attendees a thorough overview. Data is valuable, but reacting to it on time is key.

The world of IT is moving fast. Big Data is the key, but it needs to move fast too! Businesses need results sooner in order to outmaneuver competitors and seize opportunities. Fast Data Architectures are the next step for companies working on their digital transformation and moving to the cloud. In the Java world, we have multiple tools for building reactive applications like Apache Spark (Structured Streaming module), Apache Kafka (Kafka Streams module), Spring Cloud Stream,and Akka Streams. During this talk, we will review them, their use cases, and their benefits. We’ll also see that they frequently can be used together, depending upon where in the pipeline they’re used.

I have 20+ years of working experience in enterprise Java and have been involved in numerous web, mobile, and cloud native projects. Currently, our company is focused upon Big Data Engineering, with projects spanning multiple countries and clients in various sectors, e.g. banking, government, insurance/financial services, and retail. During the past 1.5 years, I’ve focused upon building real-time data processing applications using distributed systems/components such as Apache Cassandra, Apache Spark, Amazon Kinesis, and others.