Pratik Patel

We are currently moving from "AI-enabled" systems, where artificial intelligence is an additive feature, to "AI-native" systems, where intelligence is the foundational, architectural core.

Dive into the power of Amazon Managed Service for Apache Flink to build a predictive maintenance application using real-time sensor data in this hands-on live coding session.

Organizations across industries are building streaming extract, transform, and load (ETL) applications to gain meaningful insights from their datasets. Stream-processing frameworks are evolving with a surge in demand for building streaming pipelines using serverless technologies.

Developers have many options for building applications today, not just for what programming platform to use, but also what architectures are possible. Modern applications can be built using everything from monoliths to microservices to cloud functions.

Amazon Kinesis makes it easy to collect, process, and analyze real-time streaming data so that you can get timely insights and react quickly to new information. In this chalk talk, learn from best practices for building serverless streaming architectures to ingest and process billions of events per day at low latency.

One of the best ways to get timely insights and react quickly to new information that you receive from your business and your applications is to analyze streaming data. In this session, learn how, with a single click, you can start an Amazon Kinesis Data Analytics Studio notebook powered by Apache Zeppelin and Apache Flink to interactively analyze data in your stream using SQL, Python, or Scala.