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Big Data and IT-Enabled Services: Ecosystem and Coevolution?

In this article, based on a research study, author presents big data as a service-oriented and evolutionary case of disruptive IT-enabled services (IESs) rather than as datasets. Big data services emerge from combining diverse resources from an ecosystem of technologies, market needs, social actors, and other institutional contexts.

Bongsug (Kevin) Chae
on Jun 13, 2015
Analytics, Machine Learning, and the Internet of Things

In this article, author discusses the evolving technologies like Machine Learning and Internet of Things and how to exploit them for data analytics. He also talks about how organizations can benefit from these new sources of information and intelligence embedded in their environments.

Seth Earley
on Jun 07, 2015
Shaping Big Data Through Constraints Analysis

In this article, author Carlos Bueno describes a method for analyzing constraints on the shape and flow of data in systems. He talks about the factors useful for system analysis like working set & average transaction sizes, request & update rates, consistency, locality, computation, and latency. He also discusses big data architecture details of two use cases, movie streaming and face recognition.

Carlos Bueno
on May 12, 2015
AI, ML & Data Engineering

Big Data Processing with Apache Spark - Part 2: Spark SQL

Spark SQL, part of Apache Spark big data framework, is used for structured data processing and allows running SQL like queries on Spark data. In this article, Srini Penchikala discusses Spark SQL module and how it simplifies running data analytics using SQL interface. He also talks about the new features in Spark SQL, like DataFrames and JDBC data sources.

Srini Penchikala
on Apr 16, 2015
Highly Distributed Computations Without Synchronization

Synchronization of data across systems is expensive and impractical when running systems at scale. Traditional approaches for performing computations or information dissemination are not viable. In this article Basho Sr. Software Engineer Chris Meiklejohn explores the basic building blocks for crafting deterministic applications that guarantee convergence of data without synchronization.

Christopher Meiklejohn
on Feb 17, 2015
AI, ML & Data Engineering

Big Data Processing with Apache Spark – Part 1: Introduction

Apache Spark is an open source big data processing framework built around speed, ease of use, and sophisticated analytics. In this article, Srini Penchikala talks about how Apache Spark framework helps with big data processing and analytics with its standard API. He also discusses how Spark compares with traditional MapReduce implementation like Apache Hadoop.

Srini Penchikala
on Jan 30, 2015
Apache Ignite GridGain Incubator Project - Q&A Interview with Nikita Ivanov

GridGain announced that the In-Memory Data Fabric has been accepted into Apache Incubator program as Apache Ignite. InfoQ spoke with Nikita Ivanov about their product becoming part of Apache.

Srini Penchikala
on Dec 03, 2014
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.

Boris Lublinsky
on Nov 20, 2014
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.

Srini Penchikala
on Nov 13, 2014
Stats Anomalies Detector

The article describes the general outline of the Stats Anomalies Detector we developed at MyHeritage and provides a detailed explanation of how to enhance the code (will be available soon at MyHeritage GitHub) to meet your company’s needs.

Ran Levy Yonatan Harel
on Nov 07, 2014
Analytics Across the Enterprise: How IBM Realizes Business Value from Big Data and Analytics

Analytics Across the Enterprise: How IBM Realizes Business Value from Big Data and Analytics book by Brenda L. Dietrich, Emily C. Plachy, and Maureen F. Norton is a collection of experiences by analytics practitioners in IBM. InfoQ spoke with the authors about the lessons learned from the book, the arsenal of technologies IBM has about Big Data and the future of Analytics.

Alex Giamas
on Oct 27, 2014
AI, ML & Data Engineering

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.

Kai Wähner
on Sep 10, 2014