Presented By O'Reilly and Cloudera
Make Data Work
September 26–27, 2016: Training
September 27–29, 2016: Tutorials & Conference
New York, NY

Implementing extreme scaling and streaming in finance

Jim Scott (NVIDIA)
11:20am–12:00pm Thursday, 09/29/2016
IoT & real-time
Location: 1 E 12/1 E 13 Level: Beginner
Average rating: ***..
(3.80, 5 ratings)

Prerequisite knowledge

  • A general understanding of software technology
  • What you'll learn

  • Learn the core tenets of a message-driven architecture for building and managing discrete business functionality in finance
  • Description

    Agility is king in the world of finance, and a message-driven architecture is a mechanism for building and managing discrete business functionality to enable agility. In order to accommodate rapid innovation, data pipelines must evolve. However, implementing microservices can create management problems, like the number of instances running in an environment.

    Microservices can be leveraged on a message-driven architecture, but the concept must be thoughtfully implemented to show the true value. Jim Scott outlines the core tenets of a message-driven architecture and explains its importance in real-time big data-enabled distributed systems within the realm of finance. Along the way, Jim covers financial use cases dealing with securities management and fraud—starting with ingestion of data from potentially hundreds of data sources to the required fan-out of that data without sacrificing performance—and discusses the pros and cons around operational capabilities and using the same data pipeline to support development and quality assurance practices.

    Photo of Jim Scott

    Jim Scott


    Jim Scott is the head of developer relations, data science, at NVIDIA. He’s passionate about building combined big data and blockchain solutions. Over his career, Jim has held positions running operations, engineering, architecture, and QA teams in the financial services, regulatory, digital advertising, IoT, manufacturing, healthcare, chemicals, and geographical management systems industries. Jim has built systems that handle more than 50 billion transactions per day, and his work with high-throughput computing at Dow was a precursor to more standardized big data concepts like Hadoop. Jim is also the cofounder of the Chicago Hadoop Users Group (CHUG).