Sebastian Kanthak overviews Spanner, covering details of how Spanner relies on GPS and atomic clocks to provide two of its most innovative features: Lock-free strong (current) reads and global snapshots that are consistent with external events.
Avi Bryant discusses how the laws of group theory provide a useful codification of the practical lessons of building efficient distributed and real-time aggregation systems.
Kyle Kingsbury discusses some of the limitations found in distributed systems and the way some of them behave under partitioning.
Jeff Magnusson takes a deep dive into key services of Netflix’s “data platform as a service” architecture, including RESTful services that: provide comprehensive metadata management across data sources (Franklin); enable visualization and caching of results of Hadoop jobs (Sting); and visualize the execution plans produced by languages such as Pig and Hive (Lipstick).
Joshua Suereth designs a scalable distributed search service with Akka and Scala using actors, and covering practical aspects of how to scale out with Akka’s clustering API.
Oleg Zhurakousky discusses architectural tradeoffs and alternative implementations of real-time high speed data ingest into Hadoop.
Andy Gross discusses the challenges introduced by distributed systems and the need for developing new skills and tools for dealing with them.
Uri Laserson reviews the different available Python frameworks for Hadoop, including a comparison of performance, ease of use/installation, differences in implementation, and other features.
Michael Kopp explains how to run performance code at scale with Hadoop and how to analyze and optimize Hadoop jobs.
Nathan Marz shares lessons learned building Storm, an open-source, distributed, real-time computation system.
Eli Collins overviews how to build new applications with Hadoop and how to integrate Hadoop with existing applications, providing an update on the state of Hadoop ecosystem, frameworks and APIs.
Dean Wampler supports using Functional Programming and its core operations to process large amounts of data, explaining why Java’s dominance in Hadoop is harming Big Data’s progress.