Steve Wooledge is Vice President, Product Marketing for MapR, where he is responsible for identifying new market opportunities and increasing awareness for MapR technical innovations and solutions for Hadoop. Steve was previously Vice President of Marketing for Teradata Unified Data Architecture, where he drove Big Data strategy and market awareness across the product line, including Apache Hadoop. Steve has also held various roles in product and corporate marketing at Aster Data, Interwoven, and Business Objects, as well as sales and engineering roles at Business Objects and Dow Chemical. When not working, Steve enjoys juggling activities for his 5 kids and sneaking in some cycling or ski trips when he can.
The number of organizations that are thinking about using Hadoop has grown astronomically over the past year. How do you know whether you’re ready to implement Hadoop, and what are the best practices?
In the world of data warehouses and data marts, OLAP analysis has existed for many years. Concepts like drill down, drill across and roll ups have allowed business analysts and users to easily access and analyze data across a variety of dimensions such as product, customers and regions.
Cloudera’s announcement of a new open source project called Kudu, a technology described as a “complement to HDFS and Apache HBase... designed to fill gaps in Hadoop’s storage layer.” Apparently Cloudera’s development team “... eventually came to the conclusion that large architectural changes were necessary to achieve our goals”.
In this week's Whiteboard Walkthrough, Steve Wooledge, VP of Industry Solutions at MapR, talks about an Apache Sark + Hadoop use case for drug discovery that one of our customers is currently running in production.
Gartner just released a comprehensive research report based on a survey highlighting the adoption trends around Hadoop, which sheds some light on where and how customers are getting value from Hadoop. Some of the key take-aways in the report include:
Companies everywhere are excited about harnessing big data and putting it to work. Adopting a Hadoop distribution is a critical decision that has far-reaching ramifications for your organization. CITO Research recognizes this in its white paper, “Five Questions to Ask Before Choosing a Hadoop Distribution.”
MapR announced today that our SQL-on-Hadoop solution earned the highest score for Hadoop/data warehouse interoperability. MapR was among six vendors invited to participate in Gigaom Research’s January 2015 report, “Sector Roadmap: Hadoop/Data Warehouse Interoperability.” One of the key factors for our top placement in this competitive evaluation was the integration powers of Apache Drill’s technology included in the MapR Distribution. This report validates Apache Drill as a major advancement in data exploration given its schema flexibility, which makes it possible for you to immediately...
Did you know that not all Hadoop distributions are the same? As Hadoop deployments grow, the architectural differences between Hadoop distributions begin to show dramatic cost differences. These differences can save you 20-50% in terms of total cost of ownership, as we detailed in a previous post. To make it easier for you to compare distributions and understand the true costs for deploying and running Hadoop, we’ve developed the for Hadoop, a simple self-service tool that uses your own data to show how the Hadoop distributions costs stack up.
In this blog series, we’re showcasing the top 10 reasons customers are turning to MapR in order to run their data-driven businesses. Here’s reason #6: MapR provides the lowest total cost of ownership of any Hadoop distribution.
As we begin to process the data deluge from customer web interactions, mobile devices, supply chain management and the widespread use of sensors, tightly integrated data systems become critical for organizations. Harnessing data from new data sources—including the Internet of Everything—opens opportunities to provide deeper, real-time intelligence and interactions with customers and business operations.
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