Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

Serverless big data architectures: Design patterns and best practices (sponsored by AWS)

Ben Snively (Amazon Web Services (AWS))
5:25pm6:05pm Wednesday, September 27, 2017
Sponsored
Location: 1E 06
Average rating: ***..
(3.00, 3 ratings)

What you'll learn

  • Understand the concepts behind, benefits of, and best practices for serverless architectures for big data

Description

Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. But how do you incorporate serverless concepts into your big data architectures?

Ben Snively explores the concepts behind and benefits of serverless architectures for big data, providing guidance on design patterns to ingest, store, process, and visualize your data. Along the way, Ben explains when and how you can use serverless technologies to streamline data processing, minimize infrastructure management, and improve agility and robustness. You’ll also learn a reference architecture using a combination of cloud and open source technologies that will help you solve your big data problems.

Topics include:

  • Use cases and best practices for serverless big data applications
  • Serverless data lakes, ETL, real-time analytics, and ad hoc analysis
  • Leveraging AWS technologies such as Amazon DynamoDB, Amazon S3, and Amazon Kinesis for storage; AWS Lambda, Amazon Athena, and Amazon Kinesis Analytics for processing; and Amazon EMR for managed Hadoop, Spark, and Presto

This session is sponsored by Amazon Web Services.

Photo of Ben Snively

Ben Snively

Amazon Web Services (AWS)

Ben Snively is a specialist solutions architect on the Amazon Web Services public sector team, where he specializes in big data, analytics, and search. Previously, Ben was an engineer and architect on DoD contracts, where he worked with Hadoop and big data solutions. He has over 11 years of experience creating analytical systems. Ben holds both a bachelor’s and master’s degree in computer science from Georgia Institute of Technology and a master’s in computer engineering from University of Central Florida.