IBM (NYSE: IBM) is making it easier and faster for organizations to access and analyze data in-place on the IBM z Systems mainframe with the new z/OS Platform for Apache Spark. This is creating new opportunities for data scientists and developers to apply advanced analytics to the system’s rich data sets for real-time insights.
IBM Expands Access and Value of z Systems Mainframe Data with Apache Spark
April 25, 2016 by Leave a Comment
Can Spark Data Tools Stamp Out Cyber Crime?
April 15, 2016 by Leave a Comment
The video presentation below discusses how big data engines like Apache Spark are being deployed to help detect and put an end to ad fraudulence. Spark allows for enterprises across various sectors, including security firms, to extract data in real time to catch patterns and help halt fraudulent activities and breaches earlier.
Introducing Lucidworks View: Big Data Plug and Play
April 12, 2016 by Leave a Comment
A leading search and analytics software company Lucidworks today released Lucidworks View, which allows companies to quickly and easily create custom search-driven applications leveraging Apache Solr and Apache Spark.
Databricks Offers APIs to Enable Agile Application Development with Apache Spark for the Enterprise
April 1, 2016 by Leave a Comment
Databricks, the company behind Apache Spark, launched a new set of APIs that will enable enterprises to automate their Spark infrastructure to accelerate the deployment of production data-driven applications.
Syncsort Simplifies Mainframe Big Data Access, Governance and Compliance in Hadoop and Spark
March 11, 2016 by Leave a Comment
Syncsort, a global leader in Big Data and mainframe software, announced new capabilities in its industry leading data integration software, DMX-h, that for the first time, allow organizations to work with mainframe data in Hadoop or Spark in its native format ̶ essential for maintaining data lineage and compliance.
Qubole Donates Access to Big Data Cloud Platform for University Research
February 21, 2016 by Leave a Comment
Qubole, the big data as-a-service company, announced it will be donating time on the Qubole Data Service (QDS) to university classes, giving students and professors easy access to the latest, most powerful data analytics technologies on the most widely used public clouds: Amazon Web Services, Google Cloud and Microsoft Azure.
Databricks Announces Community Edition of Cloud-Based Platform
February 17, 2016 by Leave a Comment
Databricks, the company behind Apache Spark, today announced the beta release of Databricks Community Edition, a free version of the cloud-based big data platform at Spark Summit East. This service will provide users with access to a micro-cluster as well as a cluster manager and notebook environment, making it ideal for developers, data scientists, data engineers and other IT professionals to learn Spark.
Introduction to Spark Webinar
November 5, 2015 by Leave a Comment
This Introduction to SPARK webinar will feature Daniel Gutierrez, Managing Editor of insideBIGDATA.
In the past year, the Apache Spark distributed computing architecture has continued its upward trajectory amongst the big data players. Its growth has been fueled by several innovative differentiators for big data applications, such as MapReduce 2.0 (or YARN), provisions for analytic workflows, and efficient use of memory. Databricks’ recent 2015 Spark industry survey reports that Spark adoption is outpacing Hadoop because of its accelerated access to big data. In support of this new computing architecture.
The Analytics Frontier of the Hadoop Eco-System
October 15, 2014 by Leave a Comment
“The Hadoop MapReduce framework grew out of an effort to make it easy to express and parallelize simple computations that were routinely performed at Google. It wasn’t long before libraries, like Apache Mahout, were developed to enable matrix factorization, clustering, regression, and other more complex analyses on Hadoop. Now, many of these libraries and their workloads are migrating to Apache Spark because it supports a wider class of applications than MapReduce and is more appropriate for iterative algorithms, interactive processing, and streaming applications.”
