Sunday, September 15, 2013

Hadoop on Windows

Hortonworks, a leading contributor to and provider of enterprise Apache Hadoop, today announced the general availability of Hortonworks Data Platform 1.3 (HDP) for Windows, a 100-percent open source data platform powered by Apache Hadoop.  HDP 1.3 for Windows is the only Apache Hadoop-based distribution certified to run on Windows Server 2008 R2 and Windows Server 2012, enabling Microsoft customers to build and deploy Hadoop-based analytic applications. This release is further demonstration of the deep engineering collaboration between Microsoft and Hortonworks.

New functionality in HDP 1.3 for Windows includes HBase, Flume 1.3.1, ZooKeeper 3.4.5 and Mahout 0.7.0. These new capabilities enable customers to exploit net new types of data to build new business applications as part of their modern data architecture.

Hortonworks Data Platform 1.3 for Windows is now available for download at:

Friday, September 6, 2013

Intel Big Data

Intel, increasingly customizing server chips for customers, is now tuning chips for workloads in big data.

Software is becoming an important building block in chip design, and customization will help applications gather, manage and analyze data a lot quicker, said Ron Kasabian, general manager of big data solutions at Intel.

The plan includes developing accelerators or cores for big-data type workloads. For example, Intel is working with Chinese company Bocom to implement the Smart City project, which tries to solve counterfeit license plate problems in China by recognizing plates, car makes and models. The project involves sending images through server gateways, and Intel is looking to fill software gaps by enhancing the silicon.

A lot of research is also taking place at Intel labs on stream processing and graph analytics as the company designs chips and tweaks software.

Sunday, September 1, 2013

BigData Ayasdi

Ayasdi’s Insight Discovery platform highlights include automatic discoveries from complex data and operationalization of end-to-end analytic workflow tied to complex and expensive business problems. It computes across hundreds to millions of attributes to automatically find similarity amongst data points and surface hidden patterns and anomalies in the data. Users can also deploy an API to automate integration across in-house and third party applications to fully operationalize the end-to-end analytic workflow.

 Topology isn't new, but using it to analyze and understand big data is a novel idea -- one that could empower business users to find value in very large data sets without having to consult data scientists or write algorithms or models.

That's the promise of Ayasdi, a Palo Alto-based startup that uses topological data analysis to quickly glean meaning from big data.The company says its approach to analyzing big data is unique. Based on the work of Stanford University mathematics professor and Ayasdi cofounder Gunnar Carlsson, Ayasdi's enterprise-focused tools apply the abstruse concepts of topology to quickly identify relevant patterns in data.