The goal of this workshop is to build an end-to-end, streaming data analytics and recommendations pipeline on your local machine using Docker and the latest streaming analytics tools.

First, we create a data pipeline to interactively analyze, approximate, and visualize streaming data using modern tools such as Apache Spark, Kafka, Zeppelin, iPython, and ElasticSearch.

Next, we extend our pipeline to use streaming data to generate personalized recommendation models using popular machine learning, graph, and natural language processing techniques such as collaborative filtering, clustering, and topic modeling.

Lastly, we productionize our pipeline and serve live recommendations to our users!

Attendees will learn how to:

• Create a complete, end-to-end streaming data analytics pipeline
• Interactively analyze, approximate, and visualize streaming data
• Generate machine learning, graph & NLP recommendation models
• Productionize your ML models to serve real-time recommendations
• Perform a hybrid on-premise and cloud deployment using Docker
• Customize this workshop environment to your specific use cases

In this workshop we are going to-do a deep dive starting with the wire protocol and going through features and functionality with tips and tricks for how to best develop producers and consumers with Apache Kafka and continuously run production clusters.