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

Ted Malaska
Director, Enterprise Architecture, Capital One

@TedMalaska

Ted Malaska is a director of enterprise architecture at Capital One. Previously, he was the director of engineering in the Global Insight Department at Blizzard; principal solutions architect at Cloudera, helping clients find success with the Hadoop ecosystem; and a lead architect at the Financial Industry Regulatory Authority (FINRA). He has contributed code to Apache Flume, Apache Avro, Apache Yarn, Apache HDFS, Apache Spark, Apache Sqoop, and many more. Ted is a coauthor of Hadoop Application Architectures, a frequent speaker at many conferences, and a frequent blogger on data architectures.

Sessions

13:30–17:00 Tuesday, 22 May 2018
Architecting a next-generation data platform
SOLD OUT
Data engineering and architecture
 Location: Capital Suite 12 Level: Advanced
Secondary topics:  Data Platforms
Ted Malaska (Capital One), Jonathan Seidman (Cloudera)
Average rating: ****.
(4.33, 3 ratings)
Using Customer 360 and the IoT as examples, Jonathan Seidman and Ted Malaska explain how to architect a modern, real-time big data platform leveraging recent advancements in the open source software world, using components like Kafka, Flink, Kudu, Spark Streaming, and Spark SQL and modern storage engines to enable new forms of data processing and analytics. Read more.
12:05–12:45 Thursday, 24 May 2018
Big data at speed
Data engineering and architecture, Emerging technologies and case studies, Streaming systems and real-time applications
 Location: S11B Level: Intermediate
Secondary topics:  Transportation and Logistics
Mark Grover (Lyft), Ted Malaska (Capital One)
Average rating: *****
(5.00, 6 ratings)
Many details go into building a big data system for speed, from determining a respectable latency until data access and where to store the data to solving multiregion problems—or even knowing just what data you have and where stream processing fits in. Mark Grover and Ted Malaska share challenges, best practices, and lessons learned doing big data processing and analytics at scale and at speed. Read more.