InfoQ Homepage Database Content on InfoQ
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Intro to Datomic
Stuart Sierra provides an introduction to Datomic's data model, architecture, query syntax, and transactions.
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High Performance Computing Contributions to the World of Big Data
Sharan Kalwani presents the history of HPC and the technologies and trends which have contributed to creating the world of big data, covering applications of HPC resulting in big data technologies.
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Etsy Search: How We Index and Query 26 Million One-of-a-kind Items
Aaron Gardner pulls back the covers on the Etsy Search ecosystem and how they got here -- the good, the bad, and the funky.
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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.
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Cassandra, Couchbase and Spring Data in the Enterprise
The authors focus on POJO persistence over Cassandra, including automatic Cassandra schema generation and Spring context configuration using both XML and Java.
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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.
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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.
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Zen: Pinterest's Graph Storage Service
This talk goes over the design motivation for Zen and describe its internals including the API, type system and HBase backend.
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1.5 Million Log Lines Per Second: Building and Maintaining Flume Flows at Conversant
Mike Keane presents how Conversant migrated to Flume, managing 1000 agents across 4 data centers, processing over 50B log lines per day with peak hourly averages of over 1.5 million log lines/sec.
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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.
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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.
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Unified Big Data Processing with Apache Spark
Matei Zaharia talks about the latest developments in Spark and shows examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code.
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