Reporting and analysis drives businesses in making the best possible decisions. The source of all these decisions is the data. We explain the top 5 challenged for Hadoop MapReduce in the enterprise. Learn more by downloading this white paper.
Hadoop is an open-source software framework for storage and processing of large data sets on clusters of inexpensive hardware. Hadoop was created by Doug Cutting and Mike Cafarella and adopted by Apache, and is supported by a global community of contributors and users. Part of Hadoop’s appeal is that it offers a means of storing and processing very large amounts of data more cost-effectively than traditional databases or data warehouses. Learn more by downloading this white paper.
Hadoop: Moving Beyond the Big Data Hype – let’s face it. There is a lot of hype surrounding Big Data and Hadoop, the de facto Big Data technology platform. Download this guide to learn more.
Apache Hadoop, a software framework is gaining importance in IT portfolios. The solution offers a comprehensive analytic stack for big data that includes compute, storage, connectivity, enterprise Hadoop distribution with a full range of services to manage heavy workloads. Download this whitepaper to learn more.
The NetApp Open Solution for Hadoop based on E-Series storage delivers big analytics in a fiscally responsible way:
With preengineered, compatible, and supported solutions based on high-quality storage platforms
By avoiding the cost, schedule, and risk of do-it-yourself systems integration and relieving the skills gap
By avoiding substantial ongoing operational costs
Download this white paper to learn more.
Organizations are embracing Hadoop for several notable merits:
• Hadoop is distributed. Bringing a high-tech twist to the adage, “Many hands make light work,” data is stored on local disks of a distributed cluster of servers.
• Hadoop runs on commodity hardware. Based on the average cost per terabyte of compute capacity of a prepackaged system, Hadoop is easily 10 times cheaper for comparable computing capacity compared to higher-cost specialized hardware.
• Hadoop is fault-tolerant. Hardware failure is expected and is mitigated by data replication and speculative processing. If capacity is available, Hadoop runs multiple copies of the same task, accepting the results from the task that finishes first. To learn more read this white paper.
The emergence of YARN for the Hadoop 2.0 platform has opened the door to new tools and applications that promise to allow more companies to reap the benefits of big data in ways never before possible with outcomes possibly never imagined. By separating the problem of cluster resource management from the data processing function, YARN offers a world beyond MapReduce: less encumbered by complex programming protocols, faster, and at a lower cost. To learn more download this white paper.
By now almost everyone has heard the story of the yellow elephant who never forgets data, consumes whatever data you have from any source, and magically produces a big data treasure trove of business insights for you, including tweets, telemetry, customer sentiment, sensor readings, mobile app activity, and more! In fact, the story has been told and re-told so many […]
In this special guest feature, Ashish Thusoo, co-founder & CEO of Qubole, discusses how he’s seen Hadoop evolve over the past decade, what his experience was with it when it first hit the scene, where he thinks it fits in the data ecosystem today and what he believes the future holds for Hadoop.
Coho Data, a leading innovator and provider of true scale-out all flash storage architecture and infrastructure solutions for private clouds, announced DataStream 2.8, helping to make the Software-Defined Data Center (SDDC) a reality.