InfoQ Homepage Distributed Systems Content on InfoQ
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Matt Schumpert on Datameer Smart Execution
Datameer, a big data analytics application for Hadoop, introduced Datameer 5.0 with Smart Execution to dynamically select the optimal compute framework at each step in the big data analytics process. InfoQ spoke with Matt Schumpert from Datameer team about the new product and how it works to help with big data analytics needs.
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Real-Time Stream Processing as Game Changer in a Big Data World with Hadoop and Data Warehouse
This article discusses what stream processing is, how it fits into a big data architecture with Hadoop and a data warehouse (DWH), when stream processing makes sense, and what technologies and products you can choose from.
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Nikita Ivanov on GridGain’s In-Memory Accelerator for Hadoop
GridGain recently announced the In-Memory Accelerator for Hadoop, offering the benefits of in-memory computing to Hadoop based applications. It includes two components: an in-memory file system and a MapReduce implementation. InfoQ spoke with Nikita Ivanov, CTO of GridGain about the architecture of the product.
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Rich Reimer on SQL-on-Hadoop Databases and Splice Machine
SQL-on-Hadoop technologies include a SQL layer or a SQL database over Hadoop. These solutions are becoming popular recently as they solve the data management issues of Hadoop and provide a scale-out alternative for traditional RDBMSs. InfoQ spoke with Rich Reimer, VP of Marketing and Product Management at Splice Machine about the architecture and data patterns for SQL in Hadoop databases.
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Lambda Architecture: Design Simpler, Resilient, Maintainable and Scalable Big Data Solutions
Lambda Architecture proposes a simpler, elegant paradigm designed to store and process large amounts of data. In this article, author Daniel Jebaraj presents the motivation behind the Lambda Architecture, reviews its structure with the help of a sample Java application.
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Big Data Analytics for Security
In this article, authors discuss the role of big data and Hadoop in security analytics space and how to use MapReduce to efficiently process data for security analysis for use cases like Security Information and Event Management (SIEM) and Fraud Detection.
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Building Applications With Hadoop
When building applications using Hadoop, it is common to have input data from various sources coming in various formats. In his presentation, “New Tools for Building Applications on Apache Hadoop”, Eli Collins overviews how to build better products with Hadoop and various tools that can help, such as Apache Avro, Apache Crunch, Cloudera ML and the Cloudera Development Kit.
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Building a Real-time, Personalized Recommendation System with Kiji
Jon Natkins explains in this article how to create a personalized recommendation system fed with large amounts of real-time data using Kiji, which leverages HBase, Avro, Map-Reduce and Scalding.
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Costin Leau on Elasticsearch, BigData and Hadoop
Elasticsearch is an open source, distributed real-time search and analytics engine for the cloud. The first milestone of elasticsearch-hadoop 1.3.M1 was released last month. InfoQ spoke with Costin Leau about Elasticsearch and how it integrates with Hadoop and other Big Data technologies.
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Spoilt for Choice – How to choose the right Big Data / Hadoop Platform?
In his new article Kai Wähner compares several alternatives for installing a version of Hadoop and realizing big data processes. He compares distributions and tooling from Apache and many other vendors including Cloudera, HortonWorks, MapR, Amazon, IBM, Oracle, Microsoft. He additionally describes pros and cons of every distribution and provides a decision tree for choosing a most appropriate one.
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Interview and Video Review: Working with Big Data: Infrastructure, Algorithms, and Visualizations
Paul Dix leads a practical exploration into Big Data in this video training series. The first five lessons of the training span multiple server systems with a focus on the end to end processing of large quantities of XML data from real Stack Exchange posts. He completes the training with a lesson on developing visualizations for gaining insights from the macro level analysis of Big Data.
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Hadoop Virtual Panel
In this virtual panel, InfoQ talks to several Hadoop vendors and users about their views at current and future state of Hadoop and the things that are the most important for Hadoop’s further adoption and success.