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Speaker "Vladimir Bacvanski" Details Back

 

Topic

Fast Data: Big Data Analytics Using Streams

Abstract

Fast Data is the next step in the evolution of Big Data technologies. Traditional Big Data techniques are dealing with large volumes of data, but Fast Data introduces a new dimension: Velocity, which demands near real-time response to Big Data.

We begin with characteristics of Stream Processing, and then discuss the dominant architectures for Streaming systems and continue into examples of technologies and application solutions for streaming applications. We compare the important technologies, such as Storm, Spark, Flink and Apache Beam. We then discuss the use new area of Fast Data in the AI space. We conclude with implementation guide and a summary of best practices for Fast Data.
 
Outline:
•      Fast Data and Stream Processing: What is it?
•      Architectures for Stream Processing
•      Apache Storm: The Dedicated Stream system
•      Apache Spark Streaming: A versatile in-memory batch/streaming system
•      Apache Flink: Novel integration of batch and streaming
•      Apache Beam: The common API layer
•      NoSQL Data stores for Fast Data
•      Using Fast Data with AI systems
•      Best practices of mastering Fast Data
 

Profile

Dr. Vladimir Bacvanski is a Principal Architect with Strategic Architecture at PayPal. His work spans Data Platforms, Privacy, and Developer Experience as well as the introduction of Advanced Technologies. Before joining PayPal, Vladimir was the CTO and a founder of a custom development and consulting firm and has advised and worked with clients ranging from high-tech startups to financial and government organizations. Vladimir is the author of the popular O'Reilly course "Introduction to Big Data" and a coauthor of the O'Reilly course on Kafka. Vladimir received his PhD degree in Computer Science from RWTH Aachen in Germany.