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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
DevOps is Not a Feature!

DevOps is the industrialization of IT, says Nati Shalom. Organizations that wish to optimize for speed and cost cannot afford silos anymore."Doing DevOps" is not adding new features to existing tools. In this article, Shalom takes us through the differences between management solutions in a pre and post DevOps world.

Nati Shalom
on May 24, 2015
AI, ML & Data Engineering

High Tech, High Sec.: Security Concerns in Graph Databases

Graph NoSQL databases support data models with connected data and relationships. In this article, author discusses the security implications of graph database technology. He talks about the privacy and security concerns in use cases like graph discovery, knowledge management, and prediction.

George Hurlburt
on May 23, 2015
Full Stack Web Development Using Neo4j

When building a web application there are a lot of choices for the database. In this article, author discusses why Neo4j Graph database is a good choice as a data store for your web application if your data model contains lot of connected data and relationships.

Brian Underwood
on May 21, 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
Metadata-Driven Design: Designing a Flexible Engine for API Data Retrieval

Bulk data is commonly accessed via files & FTP. As the world moves toward APIs to facilitate collaboration, what are the requirements for data APIs? This article describes a meta-data driven architecture for bulk data ingestion. Two APIs operate in parallel to provide data changes as well as the data records themselves. An example demonstrates how API responses are parameterized using meta-data.

Aaron Kendall
on Apr 13, 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
Building a Mars Rover Application with DynamoDB

DynamoDB is a NoSQL database service that aims to be easily managed, so you don't have to worry about administrative burdens such as operating and scaling. This article shows how to use Amazon DynamoDB to create a Mars Rover application. You can use the same concepts described in this post to build your own web application.

Daniela Miao Kenta Yasukawa
on Jan 25, 2015
The Definitive Guide to Database Version Control

In the brave new world of big data and BI, the only technology constant is change. When it comes to database change, agility through automation - the ability to do more with less more rapidly to accelerate delivery – is what differentiates highly competitive, world-class enterprises from the rest of the crowd.

Uri Margalit
on Dec 04, 2014