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Apache: Big Data 2016

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KD

Kenneth Do

A9.com

7:30am PDT

Breakfast Regency Foyer Registration Georgia Foyer

8:00am PDT

Technology Showcase Regency Foyer

9:00am PDT

Welcome & Opening Remarks - Rich Bowen, Executive Vice President, Apache Software Foundation Regency CD

9:05am PDT

Keynote: How Netflix Leverages Big Data - Brian Sullivan, Director of Streaming Analytics, Netflix Regency CD

9:30am PDT

Keynote: How Enterprises are Leveraging Open Source Analytics Platforms for Making Game Changing Decisions - Luciano Resende, Architect, Spark Technology Center, IBM Regency CD

9:50am PDT

Keynote: It Takes a Village: Making Data Projects Work - Amy Gaskins, Big Data Project Director Regency CD

10:10am PDT

Coffee Break Regency Foyer

10:40am PDT

A Faster Way for Faster Workflows - Ken Krugler, Scale Unlimited Plaza C

11:40am PDT

Migrating Hundreds of Hadoop Pipelines into Docker Containers - Noa Resare, Spotify Plaza C

12:30pm PDT

Lunch (Attendees on Own) TBA

2:00pm PDT

SMACK Stack - Data Done Right - Stefan Siprell, codecentric AG Georgia A Will It Scale? The Secrets Behind Scaling Stream-processing Applications - Navina Ramesh, LinkedIn Plaza A

3:00pm PDT

Netflix Keystone - How We Built a 700B/day Stream Processing Cloud Platform in a Year - Peter Bakas, Netflix Georgia B

3:50pm PDT

Coffee Break Regency Foyer

4:10pm PDT

Delivering Realtime and Agile Analytics Using Apache Kafka, Spark & Drill - Neeraja Rentachintala, MapR technologies Plaza C

5:10pm PDT

Building a Durable Real-Time Data Pipeline: Apache BookKeeper at Twitter - Sijie Guo & Leigh Stewart, Twitter Georgia B

9:00am PDT

Wikimedia Content API: A Cassandra Use-case - Eric Evans, Wikimedia Foundation Plaza B

10:00am PDT

Kafka at Peak Performance - Todd Palino, Linkedin Regency A Breaking Spark: Top 5 Mistakes to Avoid When Using Apache Spark in Production - Neelesh Srinivas Salian, Cloudera Georgia B

11:20am PDT

Streaming Data Integration at Scale with Kafka - Ewen Cheslack-Postava, Confluent Regency A A Java Implementer’s Guide to Boosting Apache Spark Performance - Tim Ellison, IBM Georgia B

2:00pm PDT

Building a Self-serve Kafka Ecosystem - Joel Koshy, LinkedIn Regency A

3:00pm PDT

Hadoop Object Store - Ozone - Anu Engineer & Arpit Agarwal, Hortonworks Regency B Apache Flume or Apache Kafka? How About Both? - Jayesh Thakrar, Conversant Regency A

10:50am PDT

Spark After Dark 2.0: Complete End-to-End, Real-time Advanced Analytics, Big Data Reference Pipeline Including Machine Learning, Graph Processing, and Text/NLP Analytics, and Streaming Approximations Using Kafka... Plaza C

11:50am PDT

Introducing Datasets: Bringing Compile Time Type Checking and Functional Transformations to Spark DataFrames - Holden Karau, IBM Plaza C

2:00pm PDT

Combining Machine Learning Frameworks with Apache Spark - Tim Hunter, Databricks, Inc. Georgia A Shared Memory Layer for Spark Applications - Dmitry Setrakyan, GridGain Plaza C

3:00pm PDT

Time Series Processing with Apache Spark - Josef Adersberger, QAware GmbH Plaza C

5:10pm PDT

Less Is More: Doubling Storage Efficiency with HDFS Erasure Coding - Zhe Zhang, LinkedIn & Kai Zheng, Intel Georgia B