InfoQ Homepage Distributed Systems Content on InfoQ
-
The Revolution Will Not Be Centralized
Chris Beams shares his findings from over two years of research into bitcoin and related technologies.
/presentations/spring-data-gemfire-7-5/en/mediumimage/Chrisbig.jpg)
-
Reactive Oriented Architecture with Grails
Steve Pember discusses the tenants of the Reactive Pattern and the importance of moving away from Monolithic to Reactive architectures.
/presentations/reactive-arch-grails/en/mediumimage/Stevebig.jpg)
-
Scalable Big Data Stream Processing with Storm and Groovy
Eugene Dvorkin provides an introduction to Storm framework, explains how to build real-time applications on top of Storm with Groovy, how to process data from Twitter in real-time, etc.
/presentations/bigdata-storm-groovy/en/mediumimage/Eubig.jpg)
-
Introduction to Spring for Apache Hadoop
Thomas Risberg introduces the Spring for Apache Hadoop project and discusses integration with Spring XD, batch jobs and external data sources.
/presentations/spring-hadoop-xd/en/mediumimage/Thomasbig.jpg)
-
A Distributed Transactional Database on Hadoop
John Leach explains using HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source, showing how Hadoop/HBase can replace traditional RDBMS solutions.
/presentations/hbase-sql/en/mediumimage/Johnbig.jpg)
-
Hadoop 201 -- Deeper into the Elephant
Roman Shaposhnik discusses more advanced features of HDFS, in addition to how YARN has enabled businesses to massively scale their systems beyond what was previously possible.
/presentations/hadoop-hdfs-bigtable/en/mediumimage/Romanbig.jpg)
-
Why Would You Integrate Solr and Hadoop?
Yann Yu discusses how Solr and Hadoop complement each other, and how to use Solr as a real-time, analytical, full-text search front-end to data stored in Hadoop.
/presentations/solr-hadoop-integration/en/mediumimage/Yubig.jpg)
-
The Big Data Imperative: Discovering & Protecting Sensitive Data in Hadoop
Jeremy Stieglitz discusses best practices for a data-centric security , compliance and data governance approach, with a particular focus on two customer use cases.
/presentations/security-big-data/en/mediumimage/Jerbig.jpg)
-
Why Spark Is the Next Top (Compute) Model
Dean Wampler argues that Spark/Scala is a better data processing engine than MapReduce/Java because tools inspired by mathematics, such as FP, are ideal tools for working with data.
/presentations/spark-scala-mapreduce-java/en/mediumimage/Deanbig.jpg)
-
Customer Analytics on Hadoop
Bob Kelly presents case studies on how Platfora uses Hadoop to do analytics for several of their customers.
/presentations/customer-analytics-platfora/en/mediumimage/Bobbig.jpg)
-
Unleash the Power of HBase Shell
Jayesh Thakrar shows what can be done with irb, how to exploit JRuby-Java integration, and demonstrates how the Shell can be used in Hadoop streaming to perform complex and large volume batch jobs.
/presentations/hbase-shell/en/mediumimage/Jaybig.jpg)
-
Implementing the Lambda Architecture with Spring XD
Carlos Queiroz introduces the lambda architecture and showcases how it can be implemented with SpringXD, GemFireXD and Hadoop in a CDR(Call Detail Record) mining application.
/presentations/lambda-arch-spring-xd/en/mediumimage/Carlosbig.jpg)