Use Cases Blog Posts

Posted on February 21, 2017 by Joseph Blue

In the US, more money is spent on healthcare – about $3.2 trillion last year – than in any other discrete segment of the economy. By some estimates, that is about $600 billion more than should be spent, given the country’s size and wealth. These costs are pummeling consumers and their employers around the world.

Posted on February 17, 2017 by Ellen Friedman

Does this sound disturbing? You try to reach a particular website only to find the site is down. But it’s not that simple. You try another site – also not reachable. And another and another… You look to social media for in-the-moment reports about what’s happening and while you are reading about a huge swath of the country under cyber attack, that social media site goes out, too.

Posted on February 13, 2017 by Carol McDonald

The healthcare industry, perhaps more than any other, is on the brink of a major transformation through the use of advanced analytics and big data technologies. In this post, we’re going to talk about 5 big data trends in healthcare for 2017.

Posted on February 10, 2017 by Ronald van Loon

Businesses today need to do more than merely acknowledge big data. They need to embrace data and analytics and make them an integral part of their company. Of course, this will require building a quality team of data scientists to handle the data and analytics for the company.

Posted on February 1, 2017 by Jake Freivald

There's big potential locked away in your big data. If you can tap into it strategically, you can reap countless benefits, from increased productivity to greater profitability. You'll have a deeper understanding of your customers, better visibility into your operations, the insight you need to engage in better planning and decision making, and improved agility to respond to market and competitive shifts.

Posted on January 20, 2017 by Robert Novak

There’s a line in Alice’s Adventures in Wonderland that says, “It's no use going back to yesterday, because I was a different person then.” Some days, I feel that way about my storage infrastructures as well. I fell down a particular rabbit-hole into storage management accidentally almost 20 years ago. The typical application I supported was a database platform. With a monolithic app running on one very large server, we obviously used one very large storage array that, while slightly more modular, still had its configuration limitations.

Posted on January 18, 2017 by Jack Norris

In this week's Whiteboard Walkthrough, Jack Norris, Senior Vice President of Data and Applications at MapR, explains how the MapR Converged Data Platform opens up the use of containers to the big data environment such that you can access data directly, thus taking advantage of otherwise under utilized assets.

Posted on January 12, 2017 by Sean O’Dowd

In the past year, the big data pendulum for financial services has officially swung from passing fad or experiment to large deployments. That puts a somewhat different slant on big data trends when compared to 2016 trends. The question of big data hype versus reality has finally been put to rest for banks.

Posted on January 4, 2017 by Ted Dunning

In this week’s Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, provides some pointers for building better machine learning models, including the advantages of data streams and microservices style design in the example of a credit card fraud detector, the need for metrics, and how reconstruction of data from an auto-encoder can serve as a figure of merit that helps identify good models.

Posted on December 19, 2016 by Sameer Nori

MapR and SAP share a common belief and vision about making big data analytics enterprise- ready, both at the platform and compute engine layer. In that regard, we are pleased to integrate and support SAP HANA Vora 1.3 and believe that it offers a complete solution for all types of big data analytic use cases.

Posted on December 16, 2016 by Jake Freivald

There's one important question on everyone's mind these days: "With all this information at our disposal, how do we leverage it in more strategic ways?" The most effective way to guarantee high returns on the massive amounts of structured and unstructured information you generate and store is to make sure it is used by and shared with as many stakeholders as possible.

Posted on December 8, 2016 by Carol McDonald

This post is the second in a series where we will go over examples of how MapR data scientist Joe Blue assisted MapR customers, in this case a regional bank, to identify new data sources and apply machine learning algorithms in order to better understand their customers. In this second part, we will cover a bank customer profitability 360° example, presenting the before, during and after.

Posted on December 5, 2016 by Ronald van Loon

Business owners and executives today know the power of social media, mobile technology, cloud computing, and analytics. If you pay attention, however, you will notice that truly mature and successful digital businesses do not jump at every new technological tool or platform.

Posted on November 21, 2016 by Sean O’Dowd

The last decade has ushered in a perfect storm of disruption for the financial services sector – arguably the most data-intensive sector of the global economy. As a result, companies in this sector are caught in a vice.

Posted on November 18, 2016 by Ronald van Loon

There is no denying it – we live in The Age of the Customer. Consumers all over the world are now digitally empowered, and they have the means to decide which businesses will succeed and grow, and which ones will fail. As a result, most savvy businesses now understand that they must be customer-obsessed to succeed.

Posted on November 15, 2016 by Ronald van Loon

The field of data science is one of the youngest and most exciting fields in the technology sector. In no other industry or field can you combine statistics, data analysis, research, and marketing to do jobs that help businesses make the digital transformation and come to full digital maturity.

Posted on November 10, 2016 by Dale Kim

According to IDC, the big data market, including services such as analytics, is expected to reach nearly $50 billion by 2019. Luckily for Quantium, data is in its DNA. Australia’s largest analytics business is happily riding the coattails of the booming global big data industry.

Posted on November 9, 2016 by Jack Norris

As I discussed in my presentation at the Gartner Symposium/ITxpo in Florida, digital transformation is a key topic for business leaders today. While the impact of digital transformation is easily understood what is less clear are the steps to effectively pursue a digital transformation -- and the three keys to ensure successful digital transformation.

Posted on November 1, 2016 by Bill Peterson

MapR is pleased to support our partner Cisco with today’s UCS S-Series launch. The Cisco UCS S-Series consists of storage-optimized servers configured for scale-out storage (both Software Defined Storage and Web-Scale Storage with MapR).

Posted on October 31, 2016 by Sameer Nori

Do you remember the first time you encountered a self-service checkout terminal in the supermarket? I do. My first reaction was to ask what incentives the store was going to give me for being my own cashier? Discounts? Coupons? Surely I wasn’t expected to do someone else’s job for free.

Posted on October 31, 2016 by Sameer Nori

Do you remember the first time you encountered a self-service checkout terminal in the supermarket? I do. My first reaction was to ask what incentives the store was going to give me for being my own cashier? Discounts? Coupons? Surely I wasn’t expected to do someone else’s job for free.

Posted on October 27, 2016 by Sameer Nori

As the famous quote attributed to George Santayana goes, ‘Those who don’t learn history are doomed to repeat it.’ In business today, history is often locked away in data generated months or years earlier.

Posted on October 20, 2016 by George Demarest

This year is the first where Gartner has not included big data in any of their hype cycles. "I would not consider big data to be an emerging technology," says Burton. While this news will not affect the NASDAQ or how many artisan bagel shops there are in the SF Bay Area, it is an interesting indicator.

Posted on October 4, 2016 by Carol McDonald

With the rapid expansion of smart phones and other connected mobile devices, communications service providers (CSPs) need to rapidly process, store, and derive insights from the diverse volume of data travelling across their networks. Big data analytics can help CSPs improve profitability by optimizing network services/usage, enhancing customer experience, and improving security.

Posted on October 3, 2016 by Kirk Borne

One of the most significant characteristics of the evolving digital age is the convergence of technologies. That includes information management (structured and unstructured databases: e.g., NoSQL), data collection (big data), data storage (cloud and distributed data: e.g., Hadoop), data applications (analytics), knowledge discovery (data science), algorithms (machine learning), transparency (open data), computation (distributed data processing: e.g., MapReduce and Spark), sensors (Internet of Things: IoT), and API services (microservices, containerization).

Posted on September 27, 2016 by Rachel Silver

MapR is pleased to announce support for event-driven microservices on the MapR Converged Data Platform. In this blog post, I’d like to explain what this means, and how it fits into our bigger idea of “convergence.” Microservices are simple, single-purpose applications that work in unison via lightweight communications, such as data streams. They allow you to more easily manage segmented efforts to build, integrate, and coordinate your applications in ways that have traditionally been impossible with monolithic applications.

Posted on June 14, 2016 by Kirk Borne

In the beginning was data. How do we know this? Because many (if not all) creation stories from all cultures were essentially developed as an explanation of the world as observed by humans.

Posted on June 8, 2016 by Ellen Friedman

In this week's Whiteboard Walkthrough, Ellen Friedman, a consultant at MapR, talks about how to design a system to handle real-time applications, but also how to take advantage of streaming data beyond those in the moment insights.

Posted on June 7, 2016 by Carol McDonald

Standards and incentives for the digitizing and sharing of healthcare data along with improvements and decreasing costs in storage and parallel processing on commodity hardware, are causing a big data revolution in health care with the goal of better care at lower cost.

Posted on May 31, 2016 by Jim Scott

Just a few years ago, using a fingerprint to sign on to your phone seemed futuristic. Today, it’s everywhere and just the beginning of how biometrics will be woven into our lives.

Posted on May 18, 2016 by Jim Scott

Perhaps you’re old enough to remember when the library was the place we went to learn. We foraged through card catalogs, encyclopedias and the Reader's Guide to Periodical Literature in hopes that we’d be able to understand what was going on in other people’s minds when they decided what went where.

Posted on May 11, 2016 by Ellen Friedman

What capabilities should you look for in a messaging system when you design the architecture for a streaming data project? Let’s start with a hypothetical IoT data aggregation example to illustrate specific business goals and the requirements they place on messaging technology and data architecture needed to meet those goals...

Posted on April 27, 2016 by Sean O’Dowd

We are honored to announce that MapR was named one of the Top 10 Banking Analytics Solution Providers for 2016 by Banking CIO Outlook magazine.

Posted on April 7, 2016 by William Cairns

Having participated in a number of fantasy sports leagues and being a Data Scientist at MapR gives me a unique perspective on my approach to choosing who I think will most likely “win” the predictions for the six players, ranked in order, who I predict will most likely to finish in 10th or better place this year (and hopefully 1st) based on my statistical modeling are:

Posted on April 7, 2016 by Jack Norris

There are substantial advantages to being able to make decisions at the speed required to respond to events in the moment. In fact, real time is at the foundation of many transformational applications. Let’s take a closer look at what real time really means, and why real time is required across the entire process.

Posted on April 1, 2016 by Dale Kim

In this week's Whiteboard Walkthrough, Dale Kim, Director of Industry Solutions at MapR, describes the 540° Customer View.

Posted on March 28, 2016 by Ankur Desai

Can we agree at the outset that modern businesses rely heavily on data to make critical decisions, and the ability to make decisions in real time is very valuable? Good.

Posted on March 15, 2016 by Steve Wooledge

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.

Posted on March 14, 2016 by Joseph Blue

There are 150 quintillion (i.e. the one after quadrillion) permutations to consider when completing your NCAA bracket. Some of us don’t have time to review them all; if you are likewise short on time, you can let MapR do the heavy lifting for you and get your personalized bracket from the Crystal B-Ball!

Posted on March 2, 2016 by Kirk Borne

Dimensionality reduction is a critical component of any solution dealing with massive data collections. Being able to sift through a mountain of data efficiently in order to find the key descriptive, predictive and explanatory features of the collection is a fundamental required capability for coping with the Big Data avalanche.

Posted on February 23, 2016 by Tugdual Grall

We live in a world where the combination of Moore’s Law and Metcalfe’s Law heralds a data revolution. The billions of smartphone and broadband users today already generate massive quantities of data.

Posted on February 16, 2016 by Karen Whipple

Most likely, you’ve seen quite a few “Internet of Things” headlines in the last year. But how will the IoT really transform the world as we know it? Here are just a few ways both organizations and consumers are benefiting from IoT

Posted on February 15, 2016 by Ellen Friedman

Actionable insights from real time analytics – that’s a goal for many new projects being designed to make use of streaming data, and it’s no wonder so many organizations are aiming at this prize. If you can develop programs to process streaming data with near or actual real time analytics, you gain the ability to react to life as it happens.

Posted on February 11, 2016 by Jim Scott

For the past 25 years, applications have been built using an RDBMS with a predefined schema that forces data to conform with a schema on-write. Many people still think that they must use an RDBMS for applications, even though records in their datasets have no relation to one another.

Posted on February 10, 2016 by Jim Scott

Processing data from social media streams and sensors devices in real time is becoming increasingly prevalent, and there are plenty of open source solutions to choose from. Here is the presentation that I gave at Strata+Hadoop World, where I compared three popular Apache projects that allow you to do stream processing: Apache Storm, Apache Spark, and Apache Samza.

Posted on January 27, 2016 by Tugdual Grall

In this week's whiteboard walkthrough, Tugdual Grall, technical evangelist at MapR, explains the advantages of a publish-subscribe model for real-time data streams.

Posted on January 22, 2016 by Kirk Borne

As a lifelong computational scientist (and now data scientist) I have always been fascinated with numbers, especially lists and tables of things (= databases!).

Posted on January 21, 2016 by Jim Scott

Getting from point A to point B has been one of humanity’s greatest preoccupations throughout history. While we’ve developed new methods of transportation such as railroads, cars, trucks, and airplanes, they never seem to be fast enough.

Posted on January 19, 2016 by Jim Scott

Companies everywhere are looking for ways to improve customer service. For example, companies with call-in support centers might track how long agents take to answer calls, or how long customers stay on hold.

Posted on January 13, 2016 by Balaji Mohanam

In this week's whiteboard walkthrough, Balaji Mohanam, Product Manager at MapR, explains the difference between Apache Spark and Apache Flink and how to make a decision which to use.

Posted on January 6, 2016 by Kirk Borne

Someone once said “if you can’t measure something, you can’t understand it.” Another version of this belief says: “If you can’t measure it, it doesn’t exist.” This is a false way of thinking – a fallacy – in fact it is sometimes called the McNamara fallacy.

Posted on January 5, 2016 by Ellen Friedman

It’s the start of a new year -- we’re on the threshold of something new -- so let’s look forward to what you’re likely to be doing in 2016.

Posted on January 4, 2016 by Ellen Friedman

Banks are among the many businesses taking advantage of big data and IoT opportunities, including for mobile payments, online banking, and smart kiosks, but the huge quantities of personally sensitive data from these activities must be protected at all stages.

Posted on December 28, 2015 by Sean O’Dowd

2015 was a groundbreaking year for banking and financial markets firms, as they continue to learn how big data can help transform their processes and organizations. Now, with an eye towards what lies ahead for 2016, we see that financial services organizations are still at various stages of their activity with big data in terms of how they’re changing their environments to leverage the benefits it can offer. Banks are continuing to make progress on drafting big data strategies, onboarding providers and executing against initial and subsequent use cases. 

Posted on November 16, 2015 by Anwar Adil

Emotions should not be discarded as a distraction. Understanding a pattern in a user’s emotion is important in order for an intelligent device or system to respond appropriately. A system can exhibit “artificial emotion” to engage with the user.

Posted on October 22, 2015 by Ellen Friedman

Walmart is an industry leader in global e-commerce and brick-and-mortar retail, and they’re also a leader in the use of Hadoop-based technologies to implement their new data-driven approach to business.

Posted on October 12, 2015 by Jack Norris

The cost of waste, fraud and abuse in the healthcare industry is a key contributor to spiraling health care costs in the United States. In 2012, healthcare waste and abuse accounted for nearly $60 billion.

Posted on September 22, 2015 by Michele Nemschoff

Australian shoppers are some of the most digitally influenced in the world; a majority of Australians go online to research a product before buying it, according to a 2015 report by Deloitte.

Posted on September 15, 2015 by Michele Nemschoff

It's an exciting time for those in pharmaceutical research these days, given that research organizations can now leverage big data to improve their business.

Posted on September 10, 2015 by Steve Wooledge

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.

Posted on September 8, 2015 by Michele Nemschoff

The explosion of data from new devices and technologies has forced the telecommunications industry to completely change the way they handle big data. Their traditional storage and analytics solutions cannot adequately manage the expanding, diverse volume of data generated today.

Posted on August 21, 2015 by Jim Scott

Apache Hadoop is revolutionizing big data in more than one way. While the Hadoop platform introduced reliable distributed storage and processing, various packages such as Spark on top of Hadoop make it possible to build applications and analyze data much faster. Here are some cool ways the Hadoop stack is being used right now.

Posted on August 13, 2015 by Sean Iannuzzi

There’s a reason the industry refers to Big Data as “Big” Data. According to IBM, we create 2.5 quintillion bytes of data. Here’s another eye-opening stat: 90 percent of the data in the world today has been created in the last two years alone.

Posted on August 11, 2015 by Nitin Bandugula

Apache Spark on Hadoop is great for processing large amounts of data quickly. The story gets even better when you get into the realm of real time applications.

Posted on July 15, 2015 by Anil Gadre

You are probably all somewhere on the Spark journey to production scale—you're either at Spark Summit to learn, to start doing something with Spark, or perhaps you have mission-critical applications already running in your enterprise. On this journey, there's a lot to think about—mostly about your application—but you also need to figure out how to actually get Spark into production scale as more and more groups will want the power of the results and the value of using Spark in mission-critical, operational deployments.

Posted on July 10, 2015 by Manny Puentes

Advertising has come a long way since the days of Don Draper. While data has always played a part in ad campaigns, big data has enabled a new era of advertising.

Posted on June 11, 2015 by Sameer Nori

Reducing operating costs and increasing efficiency are, and will always be, priorities for any business, but they become imperative when an industry is facing cyclical challenges. Given the current volatility in the oil market, the oil and gas industry is looking for solutions that can proactively address inefficiencies through better asset tracking and predictive maintenance.

Posted on May 21, 2015 by Alex Gorelik

“Dad, why do they call it “Three Musketeers” when it’s all about d’Artagnan?” asked my son after we finished watching the movie. D’Artagnan was the true hero of the story, without whom there would have been no adventures.

Posted on April 3, 2015 by Ellen Friedman

Curious to know how American Express uses machine learning successfully, in production, at very large scale?

Posted on February 13, 2015 by Michele Nemschoff

Dr. Pramod Varma, Chief Architect and Technology Advisor to Unique Identification Authority of India (UIDAI), gave an informative talk titled “Architecting World's Largest Biometric Identity System - Aadhaar Experience”. He began his talk by talking about why the Aadhaar project was created. In India, the inability to prove one’s identity is one of the biggest barriers that prevents the poor from accessing benefits and subsidies. India is a country with 1.2 billion residents in over 640,000 villages. The Indian government spends $50 billion on direct subsidies (food coupons for rice, cooking gas, etc.) every year. Both public and private agencies in India require proof of identity before providing services or benefits to those living in India.

Posted on August 28, 2014 by Michele Nemschoff

Our daily commute may not feel like such a high tech experience, but whether you feel it or not – it is. Big data and Hadoop have revolutionized the transportation industry over the past several years. Whether in a car, a train, a plane or a delivery truck, we all use big data throughout our travels. Let’s go through a few specific use cases to spotlight transportation businesses that are using big data in a big way.

Posted on August 20, 2014 by Michele Nemschoff

Five minutes is easily squandered without much thought; however with Hadoop, five minutes can make a big impact.  John Schroeder, MapR CEO and Founder, recently used a five-minute keynote address to illustrate this point.  

Following is an edited transcript of John's message. 

Posted on July 8, 2014 by Barry Eggers

With Google Capital’s latest investment in MapR, it’s clear that big data and Hadoop are firmly established in the enterprise. Big data is helping enterprises across diverse industries improve their businesses through increased efficiencies and opportunities to serve their customers better.

Posted on May 5, 2014 by Jack Norris

I’m on a plane to London as I write this.  As usual, the plane is filled to capacity and coveted snack items are scarce.  The airlines must know something about passenger consumption behavior … or do they?  Accessing multiple pieces of data and analyzing the information in every imaginative way for an actionable result is what is driving Apache Hadoop technology.  MapR is helping companies take advantage of that.

Posted on August 30, 2011 by Jack Norris
Today we announced our $20M Series B funding round led by Redpoint Ventures along with our existing investors Lightspeed Venture Partners and NEA. This announcement culminates an exciting four months of activity for MapR. It started in May, with our announcement about our strategic relationship with EMC (MapR will be the basis of the Greenplum Hadoop Distribution Enterprise Edition), and at the end of June we launched the GA version of our M3 and M5 editions of the MapR distribution for Apache Hadoop.

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