Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Using a global data fabric to run a mixed cloud deployment

Jim Scott (NVIDIA)
12:05–12:45 Wednesday, 23 May 2018
Data engineering and architecture, Streaming systems and real-time applications
 Location: S11A Level: Beginner
Average rating: ****.
(4.00, 2 ratings)

Who is this presentation for?

  • Software engineers, IT administrators, and those working in line of business

Prerequisite knowledge

  • A general understanding of the cloud and data centers

What you'll learn

  • Learn why a global data fabric is a requirement for running on all cloud providers simultaneously
  • Understand the components available, how they interact, and their trade-offs

Description

Creating a business solution is a lot of work. Instead of building to run on a single cloud provider, it is far more cost effective to leverage the cloud as infrastructure as a service (IaaS). Jim Scott explains why a global data fabric is a requirement for running on all cloud providers simultaneously.

Jim covers the technology choices for implementing a business solution, including microservices, workflows for data management, and how to enhance an end-customer experience through machine learning and search engines. Jim also discusses how to leverage the data collected from a multicloud approach to ensure redundancy and high availability. While collecting all of this data, you must ensure that the data is created and managed properly, including moving the data to enabling different teams for different use cases such as security, protecting customer privacy, and ensuring full compliance with GDPR. Along the way, Jim shares his personal experiences with open source, commercial, and software-as-a-service tools he has chosen to build business solutions.

Photo of Jim Scott

Jim Scott

NVIDIA

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).