InfoQ Homepage Distributed Systems Content on InfoQ
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Peter Cnudde on How Yahoo Uses Hadoop, Deep Learning and Big Data Platform
Yahoo uses Hadoop for different use cases in big data & machine learning areas. They also use deep learning techniques in their products like Flickr. InfoQ spoke with Peter Cnudde on how Yahoo leverages big data platform technologies.
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Data Lake-as-a-Service: Big Data Processing and Analytics in the Cloud
Data Lake-as-a-Service solutions provide big data processing in the cloud for faster business outcomes in a very cost effective way. InfoQ spoke with Lovan Chetty and Hannah Smalltree from Cazena team about how Data Lake as a Service works.
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Oozie Plugin for Eclipse
Oozie Eclipse plugin is a new tool for editing Apache Oozie workflows graphically inside Eclipse. Usage of this plugin allows to skip hard to develop and maintain process definition in HPDL. Instead a process graph is defined graphically by placing process actions on pallet and connecting them. An article introduces Eclipse Oozie plugin and provides an example of its usage.
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Elixir in Action Review and Q&A with the Author
Elixir in action is a new release from Manning that aims to introduce readers to Elixir and the Erlang virtual machine while also discussing concurrent programming topics, fault-tolerance, and topics related to high-availability. InfoQ has interviewed Saša Jurić, the book's author.
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Big Data as a Service, an Interview with Google's William Vambenepe
Many of the Big Data technologies in common use originated from Google and have become popular open source platforms, but now Google is bringing an increasing range of big data services to market as part of its Google Cloud Platform. InfoQ caught up with Google's William Vambenepe, who's lead product manager for Big Data services to ask him about the shift towards service based consumption.
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F# Deep Dives Review and Author Q&A
F# Deep Dives, edited by Tomas Petricek and Phillip Trelford, is a new book aimed at showing what is the business value that using F# brings in practice. The book presents 11 real industrial scenarios and the way F# allowed field experts to solve them using a functional-first approach. InfoQ has interviewed Tomas Petricek, co-editor of the book.
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Book Review and Interview: The Practice of Cloud System Administration
The new book, The Practice of Cloud System Administration: Designing and Operating Large Distributed Systems, looks at a wide range of considerations for cloud-scale systems. In this book review and interview with the authors, we look at how teams can apply proven best practices.
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Designing a Highly Available, Fault Tolerant, Hadoop Cluster with Data Isolation
As data grows exponentially, the modern Hadoop ecosystem provides not only a reliable distributed aggregation system that delivers data parallelism, but also analytics for great data insights. In this article Monica Beckwith, starting from core Hadoop components, investigates the design of a highly available, fault tolerant Hadoop cluster, adding security and data-level isolation.
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Interview with Alex Holmes, author of “Hadoop in Practice. Second Edition”
The new “Hadoop in Practice. Second Edition” book by Alex Holmes provides a deep insight into Hadoop ecosystem covering a wide spectrum of topics such as data organization, layouts and serialization, data processing, including MapReduce and big data patterns, special structures along with their usage to simplify big data processing, and SQL on Hadoop data.
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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.