Apache Kafka lies at the heart of the largest data pipelines, handling trillions of messages and petabytes of data every day. Learn the right approach for getting the most out of Kafka from the experts at LinkedIn and Confluent. Todd Palino and Gwen Shapira demonstrate how to monitor, optimize, and troubleshoot performance of your data pipelines—from producer to consumer, development to production—as they explore some of the common problems that Kafka developers and administrators encounter when they take Apache Kafka from a proof of concept to production usage. Too often, systems are overprovisioned and underutilized and still have trouble meeting reasonable performance agreements.
Todd Palino is a site reliability engineer at LinkedIn tasked with keeping Zookeeper, Kafka, and Samza deployments fed and watered. His days are spent, in part, developing monitoring systems and tools to make that job a breeze. Previously, Todd was a systems engineer at Verisign, where he developed service-management automation for DNS, networking, and hardware management and managed hardware and software standards across the company.
Gwen Shapira is a system architect at Confluent, where she helps customers achieve success with their Apache Kafka implementations. She has 15 years of experience working with code and customers to build scalable data architectures, integrating relational and big data technologies. Gwen currently specializes in building real-time reliable data processing pipelines using Apache Kafka. Gwen is an Oracle Ace Director, the coauthor of Hadoop Application Architectures, and a frequent presenter at industry conferences. She is also a committer on Apache Kafka and Apache Sqoop. When Gwen isn’t coding or building data pipelines, you can find her pedaling her bike, exploring the roads and trails of California and beyond.
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