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Unstructured Data

Dealing with Disruptive Data: Advancing BI Connectors and Integrating SQL and NoSQL Databases

April 7, 2016 by Sumit Sarkar

Don’t count on databases to have uniform data models. Far from it, you’ll find different databases storing data in a plethora of shapes and sizes. This leads to a number of challenges when it comes time for business intelligence (BI) professionals to pull useful data from non-uniform database structures. The developers building applications versus those building intelligence have vastly different preferences in how to access data.[read more]

Apache Drill vs. Apache Spark: What’s The Right Tool for the Job?

March 1, 2016 by Jim Scott

If you’re looking to implement a big data project, you’re probably deciding whether to go with Apache Spark SQL or Apache Drill. This article can help you decide which query tool you should use for the kinds of projects you’re working on.[read more]

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No Time to Waste! 5 Essential Features for Your Information Intelligence Solution

February 29, 2016 by Luca Scagliarini

Strategic information analysis is one of the most important activities that your company can perform. The fruits of this labor, especially when consolidated throughout the organization, inform everything from marketing and innovation, to risk management activities. To achieve this level of performance, you’ll need more than a simple...[read more]

Preparing Yourself to Move to Apache Spark

February 29, 2016 by Jim Scott

While MapReduce has been the mainstay of Hadoop processing, Apache Spark is now taking the throne as the way to handle distributed computation. The reasons are obvious: Spark is very fast due to its use of Resilient Distributed Datasets, or RDDs, and it has a clean programming model.[read more]

What Are Accumulators? A Must-Know for Apache Spark

February 27, 2016 by Jim Scott

If you’ve been using Apache Spark, then you know how awesome the Resilient Distributed Dataset (RDD) is. This data structure is essential to Spark for both its speed and its reliability. There are a couple of concepts that make Spark even faster and more reliable when run over large clusters: accumulators and broadcast variables.[read more]

A Guide to Spark Streaming - Code Examples Included

February 25, 2016 by Jim Scott

Apache Spark is great for processing large amounts of data over large clusters, but wouldn’t it be great if you could process data in near real time? You can with Spark Streaming.[read more]

NoSQL Databases: 4 Game-Changing Use Cases

January 13, 2016 by Jim Scott

Sure, you’ve heard about NoSQL, but is it just another technology fad that’s all hype? What can you actually do with a NoSQL database? With the performance and scalability that NoSQL offers, especially MapR-DB, the only limit is your imagination.[read more]

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Is Big Data Winning or Losing?

December 9, 2015 by Josh Rose
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Big data is now used everywhere. AT&T has a database of 312 terabytes, the NSA use 30 million gigabytes a day and Facebook user share 30 billion pieces of content daily. There is big money and big opportunity in big data. Huge scale tools are being created all the time to benefit our existence and to make our lives easier. However these tools can sometimes be easier to cheat than other systems.[read more]

Comparing Data Science and Analytics [INFOGRAPHIC]

October 23, 2015 by Taylor Meadows

Big data is increasingly viewed as the new currency in business—and making the most of the value it holds is the key-to-the-kingdom that organizations are seeking to optimize business results. In order to do that, they need the right experts to fill the big data gap within their organizations. But who are those experts?[read more]

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The Apocalypse of Abundance: 5 Steps to End the Insanity of Information Overload

October 8, 2015 by Luca Scagliarini
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If you're like many of the people I know, the things you once enjoyed most about the Internet now make you feel overwhelmed or even left behind because you can't keep up--there's simply too much of everything.[read more]

Automate the Boring But Essential Parts of Your Data Warehouse

August 12, 2015 by Keith Peterson

To deliver on your company’s future demands for data and insights, you will need to maintain your existing data warehouse – and add the great new capabilities available with big data management and in-memory analytics. The real opportunity is in making those technologies work together smoothly with minimum effort and risk.[read more]

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Big Data Hadoop Use Cases in the Oil and Gas Industry

June 22, 2015 by Dave Mendle
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While U.S. oil production has begun expanding - so much so that the International Energy Agency predicts that by 2016 the US will surpass Saudi Arabia and Russia - the rest of the world’s oil production has ceased to expand. In an effort to streamline and optimize oil and gas production methods, advances in instrumentation, process automation, collaboration, and data management are being developed.[read more]

5 Ways That Qantas Is Using Data to Delight Customers and Build Loyalty

June 18, 2015 by Trips Reddy

While many retail, sports, manufacturing, healthcare and entertainment companies are already using data to improve customer experiences, the airline industry hasn’t caught up yet. But a handful of airlines are trying to change both this. By using a customer data platform like Umbel to merge web, mobile and social data from multiple sources, and training their staff to use this data better, airlines like Southwest, United and Qantas are using actionable data insights to deliver personalized customer experiences that delight flyers and build loyalty.[read more]

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Is Your Data Big?

June 15, 2015 by Dave Mendle
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So you’ve heard all the buzz about data. It’s big. Apparently, big data is all around us. That’s what they say anyway. Truthfully, it can be difficult as a business owner to identify where you stand in the grand universe of big data. How big is your data? How are you using it? Would you benefit from using the right big data technology? In short, how do you know if your business is part of this big data revolution? Well, let me help you answer that.[read more]

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Using Recommendation Engines to Reduce Subscription Service Churn

June 12, 2015 by Dave Mendle

Recommendation engines are the digital version of the pot of gold at the end of the Big Data rainbow. Imagine being able to know, in near-time, what your customers really want and when they want it. For a subscription service, the gold is even brighter: You can deliver your customers’ desires directly to them, leading to a dramatic reduction in churn.[read more]