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

Jesse Anderson
Managing Director, Big Data Institute

Website | @jessetanderson

Jesse Anderson is a data engineer, creative engineer, and managing director of the Big Data Institute. Jesse trains employees on big data—including cutting-edge technology like Apache Kafka, Apache Hadoop, and Apache Spark. He has taught thousands of students at companies ranging from startups to Fortune 100 companies the skills to become data engineers. He is widely regarded as an expert in the field and recognized for his novel teaching practices. Jesse is published by O’Reilly and Pragmatic Programmers and has been covered in such prestigious media outlets as the Wall Street Journal, CNN, BBC, NPR, Engadget, and Wired. You can learn more about Jesse at Jesse-Anderson.com.

Sessions

9:00am - 5:00pm Monday, September 25 & Tuesday, September 26
Secondary topics:  Architecture, Cloud, Streaming
SOLD OUT
Jesse Anderson (Big Data Institute)
To handle real-time big data, you need to solve two difficult problems: how do you ingest that much data and how will you process that much data? Jesse Anderson explores the latest real-time frameworks (both open source and managed cloud services), discusses the leading cloud providers, and explains how to choose the right one for your company. Read more.
11:20am12:00pm Thursday, September 28, 2017
Location: O'Reilly booth (Table B)
Jesse Anderson (Big Data Institute)
Jesse will talk to you about creating data engineering teams that are productive and create excellent data products. Read more.
2:55pm3:35pm Thursday, September 28, 2017
Data-driven business management, Strata Business Summit
Location: 1E 10/11 Level: Non-technical
Jesse Anderson (Big Data Institute)
Average rating: *****
(5.00, 2 ratings)
Early project success is predicated on management making sure a data engineering team is ready and has all of the skills needed. Jesse Anderson outlines five of the most common nontechnology reasons why data engineering teams fail. Read more.