Tag: apache
SE-Radio Episode 235: Ben Hindman on Apache Mesos
Ben Hindman talks to Jeff Meyerson about Apache Mesos, a distributed systems kernel. Mesos abstracts away many of the hassles of managing a distributed system. Hindman starts with a high-level explanation of Mesos, explaining the problems he encountered trying to run multiple instances of Hadoop against a single data set. He then discusses how Twitter uses […]
Episode 229: Flavio Junqueira on Distributed Coordination with Apache ZooKeeper
Flavio Junqueira is the author of Zookeeper: Distributed Process Coordination. Flavio and Jeff Meyerson begin by defining ZooKeeper and talking about what ZooKeeper is and isn’t. ZooKeeper can be thought of as a patch against certain fallacies of distributed computing: that the network is secure, has zero latency, has infinite bandwidth, and so on. With […]
Episode 222: Nathan Marz on Real-Time Processing with Apache Storm
Nathan Marz is the creator of Apache Storm, a real-time streaming application. Storm does for stream processing what Hadoop does for batch processing. The project began when Nathan was working on aggregating Twitter data using a queue-and-worker system he had designed. Many companies use Storm, including Spotify, Yelp, WebMD, and many others. Jeff and Nathan […]
Episode 219: Apache Kafka with Jun Rao
Jeff Meyerson talks to Jun Rao, a software engineer and researcher (formerly of LinkedIn). Jun has spent much of his time researching MapReduce, scalable databases, query processing, and other facets of the data warehouse. For the past three years, he has been a committer to the Apache Kafka project. Jeff and Jun first compare streaming […]
Episode 193: Apache Mahout
Recording Venue: Skype Guest: Grant Ingersoll Grant Ingersoll, founder of the Mahout project, talks with Robert about machine learning. The conversation begins with an introduction to machine learning and the forces driving the adoption of this technique. Grant explains the three main use cases, similarity metrics, supervised versus unsupervised learning, and the use of large data […]
