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Big Data Meets Big Data Analytics

admin — Sun, 27 Mar 2016 11:17:13 +0000

Organizations are inundated with data – terabytes and petabytes of it. To put it in context, 1 terabyte contains 2,000 hours of CD-quality music and 10 terabytes could store the entire US Library of Congress print collection. Exabytes, zettabytes and yottabytes definitely are on the horizon. More>>

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Forrester Market Overview for Big Data Archiving

admin — Tue, 23 Feb 2016 09:42:51 +0000

The landscape of Enterprise data is changing with the advent of Enterprise Social Data, IoT, logs, click-streams. The data is too big, moves too fast, or doesn’t fit the strictures of current database architectures. As Forrester points out, “with growing data volume, increasing compliance pressure, and the revolution of big data, enterprise architect (EA) professionals should review their archiving strategies, leveraging new technologies and approaches.” More>>

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Configuring Kafka for High Throughput

admin — Tue, 23 Feb 2016 09:33:48 +0000

In the last several years, Hadoop has evolved into an excellent and mature batch processing framework which handles the volume, veracity and variety of Big Data. However, many use cases across various domains need to handle the velocity of Big Data that Hadoop simply is not suited to handle. These use cases require real-time responses for faster decision making. Real-time analytics are also needed for large organization who generate significant log activity. These real time systems need to be able to correlate and predict events based on streaming data as it happens. More>>

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MapR Streams: Enabling Real-Time Hadoop

admin — Tue, 23 Feb 2016 09:07:04 +0000

Hadoop came to prominence because of its economical storage of data and the bulk processing capabilities it provided for extremely large data sets. Quite recently, there has been considerable interest and experimentation in applying Hadoop to data streaming applications. This has been provoked partly by the fact that Hadoop has a streaming component (Storm) and partly by Spark, which can be used in a streaming context due to its in-memory and micro-batch capability. To explain the importance of this, we’ll provide a brief explanation of what streaming is from an application perspective. More>>

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The Forrester Wave Big Data Hadoop Solutions Q1 2016

admin — Tue, 23 Feb 2016 09:05:08 +0000

MapR has also done more than any other distribution vendor under the covers of Hadoop to deliver a reliable and efficient distribution for large-cluster implementations. Its customers typically have or are planning large, mission-critical Hadoop clusters and want to use MapR-DB and MapR Streams (which implement the HBase and Kafka APIs, respectively). More>>

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Big Data in the Cloud: Converging Technologies

admin — Tue, 03 Nov 2015 06:51:28 +0000

Two IT initiatives are currently top of mind for organizations across the globe: big data analytics and cloud computing. Big data analytics offers the promise of providing valuable insights that can create competitive advantage, spark new innovations, and drive increased revenues. As a delivery model for IT services, cloud computing has the potential to enhance business agility and productivity while enabling greater efficiencies and reducing costs. More>>

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Enterprise Big Data Predictions 2015

admin — Tue, 03 Nov 2015 06:08:06 +0000

Data is now a kind of capital. It’s as necessary for creating new products, services and ways of working as financial capital. For CEOs, this means securing access to, and increasing use of, data capital by digitizing and datafying key activities with customers, suppliers and partners before rivals do. For CIOs, this means providing
data liquidity–the ability to get data the firm wants into the shape it needs with minimal time, cost and risk.More>>

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Challenges and Opportunities with Big Data

admin — Tue, 03 Nov 2015 06:05:36 +0000

The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of “Big Data”. While the promise of Big Data is real — for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 — there is currently a wide gap between its potential and its realization.More>>

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Big data analytics

admin — Tue, 03 Nov 2015 06:03:39 +0000

People, devices and networks are constantly generating data. When users stream videos, play the latest game with friends, or make in-app purchases, their activity generates data about their needs and preferences, as well as their QoE. Even when users put their devices in their pockets, the network is generating location and other data that keeps services running and ready to use. As a result, the rate of mobile network data traffic growth is increasing rapidly. It is estimated that by 2020, the number of smartphone subscriptions will have increased from today’s 2.7 billion to 6.1 billion, and the total amount of mobile traffic generated by smartphones will be five times
that of today.

More>>

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The Future of Big Data and Retail with Case Studies

admin — Tue, 06 Oct 2015 12:20:35 +0000

This article is the sixth and final in an editorial series with a goal of directing line of business leaders in conjunction with enterprise technologists with a focus on opportunities for retailers and how Dell can help them get started. The guide also will serve as a resource for retailers that are farther along the big data path and have more advanced technology requirements. More>>

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