Tomer Shiran
Vice President, Product Management, MapR

Tomer Shiran heads the product management team at MapR and is responsible for product strategy, roadmap and requirements. Prior to MapR, Tomer held numerous product management and engineering roles at Microsoft, most recently as the product manager for Microsoft Internet Security & Acceleration Server (now Microsoft Forefront). He is the founder of two websites that have served tens of millions of users, and received coverage in prestigious publications such as The New York Times, USA Today and The Times of London. Tomer is also the author of a 900-page programming book. He holds an MS in Computer Engineering from Carnegie Mellon University and a BS in Computer Science from Technion - Israel Institute of Technology.

Author's Posts

Posted on October 22, 2013 by Tomer Shiran
Apache Hadoop has inspired a rich ecosystem of projects and products that benefit everyone interested in Big Data. With so many options available, I frequently am asked whether MapR supports a specific project - examples include Impala, Knox, Storm and Falcon - so I thought it would make sense to provide an overview that will help explain how this works for MapR. There are hundreds of open source and commercial projects that relate to Hadoop in one way or another. These projects can be divided into two categories:
Posted on September 13, 2013 by Tomer Shiran
Gartner Analyst Merv Adrian is spot on in his blog What, Exactly is ‘Proprietary Hadoop’? Proposed: ‘distribution-specific’.
Posted on August 7, 2013 by Tomer Shiran

SQL has become really hot – Why? Customers are looking for interactive performance in big data solutions with streamlined work flow and flexibility in their choices. Being able to use SQL effectively on Hadoop and other big data systems is a big step toward meeting that goal.

Posted on July 15, 2013 by Tomer Shiran

MapR’s competitors have been presenting “futures” to prospects since the day MapR came out of stealth. For the most part, the “futures” that were presented in 2011 are still futures in 2013.

Posted on July 11, 2013 by Tomer Shiran
One year ago we announced a strategic OEM partnership with Amazon Web Services (AWS), making the MapR M3 and M5 Editions for Hadoop available on Amazon Elastic MapReduce as a simple drop-down selection in the AWS Management Console.
Posted on March 4, 2013 by Tomer Shiran
This was an exciting week at the Strata Big Data conference. Our CEO, John Schroeder delivered a short keynote, Ted Dunning presented on moving Beyond Hadoop and included a glimpse of real-time streaming with MapR and Storm integration. I also presented an overview of Apache Drill in a standing room only session.
Posted on September 8, 2012 by Tomer Shiran
It has been an exciting time for us here at MapR with the unveiling of the Apache Drill project and the enormous amount of interest in the project. Many in the industry have commented to me about Drill being a game changer for Hadoop as well as the larger Big Data community.
Posted on May 11, 2012 by Tomer Shiran
I’m happy to announce that we just released the MapR Hive ODBC Driver. It is available to all MapR M3 and M5 users. The MapR Hive ODBC Driver is a standard ODBC 3.52 driver, allowing our users to leverage hundreds of commercial and open source SQL-based tools, such as query builders and BI applications. For example, we’ve tested our Hive ODBC Connector with Excel, Tableau, MicroStrategy and a variety of 100% open source SQL tools, such as Kaimon (http://www.kaimon.cl).
Posted on January 26, 2012 by Tomer Shiran
After joining MapR back in 2009, I spent many months meeting with early Hadoop users and listening to their pain points. In many of these meetings, users described problems related the HDFS architecture and the NameNode in particular. In this blog post I wanted to share 10 NameNode-related issues that came up frequently in these meetings:
Posted on December 6, 2011 by Tomer Shiran
Today we announced version 1.2 of the MapR Distribution for Apache Hadoop. With this release, MapR continues to push the envelope by making Hadoop more accessible to more users, more languages, and more platforms. This release includes numerous features and capabilities including:

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