Online Hadoop Training Attracts Big Data Crowd

Aspiring big data professionals are embracing online Hadoop training courses. The evidence: More than 10,000 people registered for MapR's free Hadoop on-demand training during the courseware's first month of availability.

Continue reading here on +Information Management

Gender equality report: an example of how big data can address big problems (includes a video)

For the 'No Ceilings' report, researchers collected 850,000 gender-related data points over a 20-year period from the UN, World Bank, and other organizations to measure progress.

Read and watch the video http://www.csmonitor.com/USA/USA-Update/2015/0309/Gender-equality-report-an-example-of-how-big-data-can-address-big-problems-video on +CSMONITOR

Navigating the Hadoop ecosystem

A field guide to the Apache Hadoop projects, subprojects, and related technologies.

IT managers, developers, data analysts, and system architects are encountering the largest and most disruptive change in data analysis since the ascendency of the relational database in early 1980s — the challenge to process, organize, and take full advantage of big data.

Continue reading this article here on +O'Reilly

Smart cities will house 9.7 billion IoT devices by 2020: Gartner

Summary:Gartner says a majority of IoT spending for smart cities will come from the private sector, which is good news for the TSPs that stand to benefit most in terms of revenue.

Read about it here on +ZDNet

Amazon Accelerates Internet of Things (IoT) Strategy

Amazon's Internet of Things (IoT) strategy is coming into public focus. The latest move involves Amazon acquiring 2lemetry -- an IoT startup that links machine data with business applications, and potentially drives customers to buy more products and services.

Continue reading here on +Information Management

SLIDESHOW: 10 Big Data Software Requirements

What are the core software components in a big data solution that delivers analytics? Although requirements certainly vary from project to project, here are ten software building blocks found in many big data rollouts.

See the slideshow here on +Information Management

Analytics Won't Cure Stupidity

Every now and then you'll see a reference in the news or in general conversation regarding how some big data or analytics project failed.

If it's a big-money failure, particularly in the government realm, you can be sure that it will be high profile, and that it will bring the "critics of everything data" out of the shadows. We've seen these stories occasionally as big data has caught the public eye, and you can bet that with more organizations turning to analytics and big data we'll see even more projects gone bad. One side effect will be that the failures will taint the success stories.

Continue reading here on +All Analytics

How PayPal is Using Deep Learning to Root Out Fraud

Writing for GigaOM, Derrick Harris recently stated, “Hui Wang has seen the nature of online fraud change a lot in the 11 years she’s been at PayPal. In fact, a continuous evolution of methods is kind of the nature of cybercrime. As the good guys catch onto one approach, the bad guys try to avoid detection by using another. Today, said Wang, PayPal’s senior director of global risk sciences, ‘The fraudsters we’re interacting with are… very unique and very innovative. …Our fraud problem is a lot more complex than anyone can think of.’ In deep learning, though, Wang and her team might have found a way to help level the playing field between PayPal and criminals who want exploit the online payment platform.”

Continue reading here on +DATAVERSITY

EMC: Can Data Lakes Create Big Data Splash?

With one announcement, EMC Corp. hopes to solidify its big data strategy while also convincing data scientists and investors that three closely aligned companies (EMC, Pivotal and VMware) are better than a big corporate breakup.

Read about it here on +Information Management

The Mathematics of Data: Graph Analytics as a Service

Graph databases are optimal for running advanced analytics because they indicate the relationship between data elements and allow for readily discernible inferences between them—yielding answers to questions that users never thought to ask. Leveraging the prowess of graph analytics (especially on Big Data sets), however, has traditionally been hampered.

Continue reading about it here on +DATAVERSITY

How to Keep Machine Learning From Becoming a Big Data Bottleneck

Jack Vaughn of SearchDataManagement recently wrote, “The recent rush of machine learning technology and products is formidable, but machine learning techniques are far from new. What's new is the number of parallelized data processing platforms becoming available for machine learning applications of big data. At the recent Strata + Hadoop World conference in San Jose, Calif., data specialists said the complexity of predictive machine learning algorithms and models, as well as the sheer numbers of such models, can limit use of machine learning in large corporations.

Continue reading here on +DATAVERSITY

How Telecoms Can Adapt to the Internet of Things

As the Internet of Things continues to evolve, many companies, especially network operators, are struggling with rapid increases in data volume. Data load predictions of 24 – and even 12 – months ago have long been surpassed. This creates a tremendous amount of strain on infrastructures that did not predict the dramatic increase in the amount of data coming in, the way the data would need to be queried, or the changing ways that business users would want to analyze data.

Continue reading here on +Data Informed

Inside Google's Insurance Data Strategy

When the long-awaited Google Compare for auto insurance aggregator launched last week, the largest U.S. P&C insurers were conspicuously absent. Industry observers commented that there was a latent fear that Google could “reverse-engineer” the auto insurance business through its connection points with carriers. So by their absence, the largest insurers were sending a message that they don’t want to give away core parts of their business such as their rating algorithms to a data- and cash-rich company that has been rumored to have eyes on being a carrier.

Continue reading here on +Information Management

Database Archaeology

Few database projects start with a clean slate. Many operational applications have legacy databases as a source of data and ideas. Analytical applications have the operational databases that are feeding data. Developers often encounter existing databases that are poorly documented and need to figure them out. We use the term database archaeology to refer to the study of database artefacts

Continue reading here on +DATAVERSITY .


I've had to do this many times myself.  You have to scour for documentation, find long standing employees, and worst case scenario reverse engineer data models and read through code to work out what is going on.  There is a way to get 80% fairly easily and the rest you have to work out using data and traces ad comments in the code.

Unlocking Value From Hadoop Analytics

Many organizations have deployed Hadoop to gain additional value from big data. Most envisioned they could use Hadoop to easily combine and analyze large data sets to identify new market opportunities or better detect fraud. However, combining data sets within Hadoop is easier said than done. Most organizations, thus far, have faced obstacles and failed to achieve estimated return on investment from their Hadoop deployments.

Continue reading here on +Information Management

Use cases: Applying Big Data Analytics to transform business KPIs for CSPs

CSPs are operating in a competitive and dynamic environment. In order to sustain their growth and market share in a highly competitive world, they need to leverage the full potential of their assets of network and subscriber data. Big Data analytics will enable quick experimentation with large volume of variety data, replace human decisioning, create transparency across business operations, enable micro segmentation and bring in innovative business models, products and pricing.

Continue reading here on +Flytxt

Feature Engineering: How to transform variables and create new ones?

One of common advice machine learning experts have for beginners is – focus on Feature Engineering. Be it a beginner building his first model or some one who has won Kaggle competitions – following this advice works wonders for every one!

Sunil Ray has personally seen predictive power of several models improve significantly with application of feature engineering.

Continue reading his article here on +Analytics Vidhya

How Big Data and Internet of Things (IoT) Impact Data Centers

Big data just keeps expanding and expanding. Science Daily reported in 2013, a full 90 percent of all the data in the world was generated over the previous two years. Jorge Balcells, Director of Technical Services, Verne Global, noted that with 2.5 billion Internet users worldwide, and with about 250 million users in United States alone, the level of users has exploded, particularly in the last decade.

Continue reading here on +Data Center Knowledge

Big Data Every Day Keeps The Doctor Away

Healthcare has been put into the hands of the people – more specifically, the handheld devices of the people.

Digital technology has recently become a major source of health information for people around the world on topics as diverse as exercise, diet, pregnancy, sleep, and mental health.

Continue reading here on +BusinessIntelligence.com

Analytics Empower Your Sales Team

All lines of business are under pressure to meet targets and deliver expected results, but none is under more pressure than Sales. Like other organizations it must use information to derive insights about progress and problems and to decide what changes to make. Today businesses collect and analyze data from more data sources in more forms than ever before. To understand it they need effective analytics, and again none need it more than Sales.

Continue reading here on +Information Management

7 common mistakes when doing Machine Learning

In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data. For Big Data, it pays off to analyze the data upfront and then design the modeling pipeline accordingly.

Read about them here on +KDnuggets

Are Chief Data Officers (CDOs) A Short-term Fix?

Are chief data officers (CDOs) a short-term fix -- meant to fill a painful business gap between CIOs and chief marketing officers? That's the thesis from Forrester Research VP Carlton Doty -- who shared a range of IT leadership opinions during the Adobe Summit this week in Salt Lake City.

Read more about it here on +Information Management

Making big data smaller

SUMMARY: Data science is the key to unlocking value from big data, but it must be combined with micro-vertical domain expertise, writes Infor president Duncan Angove.

Data is all around us. It’s in the computers we use. It’s in the machines we operate. It’s in a huge pile after a single hospital visit.

Consider:

30 MB of data are required to store an X-ray
150 MB are required to store a single MRI
3 GB are required to store the human genome
660 terabytes are necessary to power a single hospital

Continue reading here on +Diginomica

How to Tell a Story with Millions of Rows of Data

Ellie Fields of Tableau the addresses the misconception that data stories are for small data sets and explains how to identify the narrative that emerges from huge amounts of information.

How many stories does New York City hold? Eight million, says the famous film “The Naked City.”

“That’s a lot of data,” say people in our world.

Data storytelling is something that people who work with data are talking about quite a lot. The success of news outlets focused on data journalism – FiveThirtyEight, Vox, and The New York Times’ Upshot, for example, is proof of this.

Continue reading here on +Data Informed

The End of Big Data: AI and the Rise of the Narrative

Kris Hammond believes in data and loves the world of big data and the analysis that it affords. However, we are at an inflection point with regard to our ability to utilize data, and it’s reflected in a growing dissatisfaction in the C-Suite.

In a recent Capgemini Consulting survey, less than a quarter of respondents with big data initiatives said they considered them a success, and under 10 percent were “fully satisfied” with the results.

Continue reading here on +Data Informed

How UPS Uses Big Data With Every Delivery

The different routes that can get a driver from point A to point B are overwhelming. What determines the best course to take? Is it the one with less traffic, the least amount of stoplights, the highest speed limit or the least amount of physical distance to travel? For many, the most efficient route isn’t a major concern, but for companies like UPS, minimizing driving time is crucial.

Continue reading about this here on +BusinessIntelligence.com

9 Must-Have Skills to Land Top Big Data Jobs in 2015

The secret is out, and the mad rush is on to leverage big data analytics tools and techniques for competitive advantage before they become commoditized. If you’re in the market for a big data job in 2015, these are the nine skills that will garner you a job offer.

Read about the 9 skills here on Datanami

Real-Time Analytics and the Internet of Things are a Perfect Match

The Internet of Things is here and is here to stay. With billions of devices already connected and another trillion coming our way in the next decade, organizations should prepare themselves for a data flood. A data flood that can generate real-time insights in how the organization is performing. Using a plethora of data sources, such as smart meters, in-store sensors, medical devices, automotive sensors and wearables, organization can start to optimize their business.

Continue reading here on +Datafloq

Big Government Is Getting In The Way Of Big Data

When the government wants to know how many people are unemployed, it calls people and asks them whether they’re working. When it wants to know how quickly prices are rising, it sends researchers to stores to check price tags. And when it wants to know how much consumers are spending, it mails forms to thousands of retailers asking about their sales.

Continue reading here on +Fivethirtyeight

4 Misconceptions About 'Big Data' You Can Stop Believing

Big data has become a hot topic in the past five years or so, but it has been providing insights for hundreds of years. For example, the first U.S. census was taken in 1790, the Hollerith tabulating machine was created in the late 1880s, and in 1944 Fremont Rider was already envisioning that the Yale Library would have more than 200 million volumes by 2040.

Read this article by Dan Hogan from +LiveScience here

SLIDESHOW: 15 Chief Data Officer Job Requirements

Sixty-one percent of CIOs want to hire chief data officers (CDOs) within the next 12 months. At a high level, CDOs oversee how data is gathered, managed, protected, and monetized. But what else fits into a CDO job description? Here are 15 requirements for the position.

Read the slideshow here on +Information Management

Hadoop High 5 with IBM's Anjul Bhambhri

In the thought leadership series called 'Hadoop High 5', they ask leaders in big data and hadoop ecosystem on the vision and the future path. Continuing this series, they asked Anjul Bhambhri five questions. Anjul Bhambhri is the Vice President of Big Data at IBM. She was previously the Director of IBM Optim application and data life cycle management tools. She is a seasoned professional with over twenty-five years in the database industry. Over this time, Anjul has held various engineering and management positions at IBM, Informix and Sybase. Prior to her assignment in tools, Anjul spearheaded the development of XML capabilities in IBM's DB2 database server. She is a recipient of the YWCA of Silicon Valley's “Tribute to Women in Technology” award for 2009. Anjul holds a degree in Electrical Engineering.

Read it here on Hadoopsphere

What is Behaviour Driven (Database) Development?

Behaviour Driven Development (BDD) is not always clearly understood, and the term is particularly unfamiliar in database circles. Seb Rose introduces us to the fundamentals of BDD, and make some suggestions for how it might be relevant to database development.

It can be found here on +Simple Talk

Suggest Compression Strategies for Tables and Indexes

Greg Low talks though and generously shares in his blog the script he uses to determine which compression strategy to use on tables.  As part of that the cutoff points are configurable but his default values are as shown:

CCI (Clustered Columnstore Index) will be recommended where the partition is scanned more than 95% of the time, updated less than 10% of the time, seeks and lookups are less than 5% of the time, and where there are at least 800,000 rows. It will also only be recommended if it is supported.
PAGE will be recommended where the partition is scanned more than 75% of the time and updated less than 20% of the time.
ROW will be recommended in all other cases. We believe that ROW should be the default in SQL Server across the board, instead of NONE.

Cisco, Hadoop Providers Partner on Big Data in Converged Data Centers

Cisco Systems is partnering with leading Hadoop companies (names like Cloudera, Hortonworks and MapR) to move big data applications onto Cisco's converged data center offerings.

The integration goes beyond basic Hadoop-on-Cisco hardware offerings.

Read about it here on +Information Management

5 Ways Businesses Can Optimize Social Data

Social networks have grown by leaps and bounds, and will continue to do so in the coming years. With so much social media data to sift through, businesses have come to depend on listening platforms more heavily before drawing up any new marketing programs. Such software tools enable us to capture, manage, and analyze social data accurately, and there’s no denying that it is a rich source of information. However, few marketers know exactly how to use the data to their full advantage.

Continue reading here on this blog from +Infinit Datum

EBOOK: Hadoop Virtualisation

Hadoop is a popular framework used for nimble, cost-effective analysis of unstructured data. The global Hadoop market, valued at $1.5 billion in 2012, is estimated to reach $50 billion by 2020. Companies can now choose to deploy a Hadoop cluster in a physical server environment, a private cloud environment, or in the public cloud. Thanks to VMware for sponsoring this free report.

Get this free ebook here from +O'Reilly

Looking to Use Big Data? Ask These 4 Questions.

Data streams are constantly flowing from the technology we use in our daily life. Phones, televisions, computers, credit cards and even sensor-equipped buildings are all contributing to the data stream. And all this data is not only growing in volume, but it’s growing at a monstrous speed, doubling in size every two years. It is predicted that the data we create every year will reach 44 zettabytes, or 44 trillion gigabytes by 2020.

Continue reading here on +Entrepreneur

Five Potentially Lethal Information Management Weaknesses

With the big data revolution underway, managing information is harder than ever. Businesses may find, perhaps too late, that their information management strategy is not providing the value they need. Here are five land mines of information management, and how to avoid each one of them.

Read about them here on +Information Management

The Future of Information: Revealed

Whenever one pulls out their crystal ball to see what the future may hold for information (in society, business, and our personal lives) it is relatively easy to reference the “usual suspects.” These include more mobile device applications, the “Internet of Things (IoT)” via machine-to-machine sensors, and an electronic wallet (like a highway toll EZPass). But it's time to get ready for so much more.

Continue reading here on +Information Management

The Consumption Mindset: Turning Analytics into Action

In 1963, Bernard Forscher wrote a fascinating letter in a science journal -- in which he likened scientists to builders tasked with making edifices (explanations or laws) out of bricks (facts). These ‘builders’ became so obsessed with making bricks that over time there were more bricks than edifices. Bernard concludes his story with the assessment that builders were no longer making a distinction between a pile of bricks and edifices.

Continue reading here on +Information Management

More CEOs Discover Power of Analytics

CEOs are looking to digital tools such as data analytics, mobile, cybersecurity and the Internet of Things to help them seize bigger opportunities for their organizations, according to a new study from consulting firm PwC.

A majority of the 1,322 worldwide CEOs surveyed by the firm (84%) say digital technologies are creating “quite high” or “very high” value through data and data analytics—second only to value through operational efficiencies (88%)

Read more here on +Information Management

CoVennTree: a new method for the comparative analysis of large datasets

The visualization of massive datasets, such as those resulting from comparative metatranscriptome analyses or the analysis of microbial population structures using ribosomal RNA sequences, is a challenging task. We developed a new method called CoVennTree (Comparative weighted Venn Tree) that simultaneously compares up to three multifarious datasets by aggregating and propagating information from the bottom to the top level and produces a graphical output in Cytoscape.

Read about it here on Frontiers

5 reasons when to and when not to use Hadoop

Hold on! Wait a minute and think before you join the race and become a Hadoop Maniac. Hadoop has been the buzz word in the IT industry for some time now. Everyone seems to be in a rush to learn, implement and adopt Hadoop. And why should they not? The IT industry is all about change. You will not like to be left behind while others leverage Hadoop. However, just learning Hadoop is not enough.

Read all about it here on the +edureka! blog

Big Data and Small Businesses: Why Use It and How to Make it Work

More and more enterprises all over the world are leveraging big data for their businesses. For small businesses, however, it’s another story.

The misconception is that big data is just for big businesses. In fact, a research called Top 10 SMB Technology Trends for 2014 done by the SMB Group states that although SMBs understand the value of getting information for their businesses, only 18% of small businesses and 57% of medium scale businesses use business intelligence and analytics solutions.

Read more here on +Infinit Datum

EBOOK: The Elements of Data Analytic Style

A guide for people who want to analyze data.

Data analysis is at least as much art as it is science. This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks. It is based in part on the authors blog posts, lecture materials, and tutorials

Download this ebook by +Jeff Leek  from here.

White House Hires Its First Data Scientist

Data scientists have hit the big time. We know this because The White House is hiring one.

Dhanurjay “DJ” Patil was recently named the White House’s first data scientist—his official title is Chief Data Scientist and Deputy Chief Technology Officer for Policy at the White House.

Continue reading here on +Laserfiche

With big data getting captured, biologists look for life-saving patterns

At the startup Bugworks in Bangalore, a few biologists and computer scientists are working on an old problem: finding drugs to fight microbes. Drug researchers around the world are increasingly interested in this topic, as bacteria have been fighting back and developing resistance to old drugs. Finding new non-toxic antibiotics is not an easy job, but biologists have a few new tricks up their sleeve.

Continue reading here on +The Economic Times

Forecasting the fashion future: Big Data comes to rescue fashion designers!

For years, fashion industry has had previous data and intuition at its disposal to predict customer demands which is now becoming quite irrelevant considering the fast-changing fashion trends and the tough competition in the market. More so, with more and more people getting brand conscious, it is becoming tougher for aspiring fashion designers to make a place on the mannequins. But they need not worry; Big Data is here to save the budding talent!

Continue reading here on +Big Data Made Simple

Balancing Freedom and Control to Enable Governed Data Discovery

Data governance is – now maybe more than ever – a charged topic.

Business-driven data discovery is becoming fundamental for organizations to explore, iterate, and extract meaningful and actionable insights from vast amounts of new and diverse internal and external data.

Read more here on +Information Management

Processing frameworks for Hadoop

How to decide which framework is best for your particular use case.

Hadoop has become the de-facto platform for storing and processing large amounts of data and has found widespread applications. In the Hadoop ecosystem, you can store your data in one of the storage managers (for example, HDFS, HBase, Solr, etc.) and then use a processing framework to process the stored data. Hadoop first shipped with only one processing framework: MapReduce.

Continue reading here on +O'Reilly

Business Model Innovation Needs to Be More of a (Data) Science

Here's a blog by Jerry Overton, created to accompany his talk at last week's Strata + Hadoop World on business model innovation needing to be more of a (data) science. Jerry is head of advanced analytics research in CSC's ResearchNetwork and founder of CSC's FutureTense competency, which includes the Predictive Modeling Research Group, Advanced Analytics Lab and Predictive Modeling School.

Read it here.

Signals from Strata + Hadoop World in San Jose, CA, 2015

If you attended last week's sell-out Strata + Hadoop World, you know how huge it was—and how hard it is to give a concise recap. But Jenn Webb +O'Reilly  has made a noble effort to sum up the key insights from Strata + Hadoop World in San Jose, CA, 2015.

You can read them here on the +O'Reilly 

Understanding the Chief Data Officer

To manage today’s flood of available data, a number of high-profile corporations have adopted a new position in addition to existing CTOs and CIOs: the Chief Data Officer, or CDO. In this report, Julie Steele provides a clear, concise look at how CDOs view their nascent role in high-profile organizations such as Wells Fargo, Samsung, the Republican National Committee, Allstate, and the Federal Reserve Board.

Download your copy here from +Silicon Valley Data Science  (you will have to give email address and name/company.

A New Generation Of Analytics Offers Help For Sales

All lines of business are under pressure to meet targets and deliver expected results, but none is under more pressure than Sales. Like other organizations it must use information to derive insights about progress and problems and to decide what changes to make. Today businesses collect and analyze data from more data sources in more forms than ever before. To understand it they need effective analytics, and again none need it more than Sales.

Continue reading here on +BusinessIntelligence.com

Find the Needle in the Semi-Structured Haystack

Social media data offers a veritable goldmine of insights about users. But companies struggle to transform social data and combine it with existing data sets to enrich analysis. Ayush Parashar of UNIFi describes the steps from data discovery to insight.

Read it here on +Data Informed

IBM, Juniper Converge Big Data & Network Intelligence

(Bloomberg) -- International Business Machines Corp. formed a partnership with Juniper Networks Inc. to create networks with built-in analytics, harnessing big data to improve mobile and other applications.

IBM and Juniper will design and sell tools to help businesses analyze information, update operations, reduce costs and improve how applications run, according to a statement Tuesday.

Continue reading here on +Information Management

Who Owns Your Big Data?

Your customers -- users themselves -- must remain the ultimate owner of their own data.  This blog discusses that ownership in the light of open data and looks at an example.

Read the blog here on +Information Management

Big Data: Welcome to Awkward Teen Years

Just a few years ago, when big data was associated primarily with Hadoop, it was like a precocious child…fun for adults, but nobody took it seriously. Brian Hopkins attended Strata in San Jose this February, and he can see things have changed. Attendance doubled from last year and many of the attendees are the business casual managers – not the blue jeaned developers and admins of days gone by. Big data is maturing and nobody takes it lightly anymore.

Continue reading his blog here on +Information Management

Gartner Magic Quadrant articles roundup

Each time Gartner unveils its annual Magic Quadrant for Business Intelligence and Analytics platforms, most media and industry chatter mentions specific BI vendors and their relative market positions. But here's how Chief Data Officers (CDOs) can study the research to actually determine the BI capabilities they need.  Read +Information Management 's take on it here.

Read the Gartner report here

There is also a slideshow of the 17 BI & Analytics Requirements: Gartner Magic Quadrant here from +Information Management

Near real-time queries produce more credible results for big data users plus SLIDESHOW Big Data: 14 Requirements for Real-Time Analytics

The near-real time query capability that a company's technology enables helps users avoid the wait times they encounter while big data processors are finishing analysis and reporting tasks..

Continue reading here on +Tech Republic

Also this seems a slightly different take on the same subject:

Slideshow on Big Data: 14 Requirements for Real-Time Analytics

To build a successful real-time business analytics system, you'll likely need an in-memory database plus several other core technologies. Here's a look at each potential piece of a real-time analytics solution. Special thanks to Wikibon for background information.

See the slideshow here on +Information Management

SLIDESHOW: Big Data: 14 Requirements for Real-Time Analytics

To build a successful real-time business analytics system, you'll likely need an in-memory database plus several other core technologies. Here's a look at each potential piece of a real-time analytics solution. Special thanks to Wikibon for background information.

I definitely agree with #7

Continue to read here on +Information Management

Microsoft delivers public preview of mobile real-time analytics on Azure

Microsoft has made available a public preview test build of its new mobile real-time analytics service running on Azure.

Officially christened "Azure Mobile Engagement," the new service is built on technology Microsoft acquired when it bought Paris-based Capptain in May 2014.

Continue reading here on +ZDNet

The proof is in the testing: The Swiss breakthrough that will make software more reliable

The size and complexity of today's software programs can make it difficult to check their likely reliability.

Testing only goes so far: often after applications are released, it's a wait-and-see strategy, with developers sending out patches for products if and when major problems become evident.

Two computer scientists at the Swiss Federal Institute of Technology (École Polytechnique Fédérale de Lausanne or EPFL) hope to change that - using automated reasoning tools to replace validating software through testing with more accurate formal mathematical proofs.

Continue reading here on +ZDNet

New Azure services help more people realize the possibilities of big data

This week in San Jose thousands of people are at Strata + Hadoop World to explore the technology and business of big data. As part of our participation in the conference, we are pleased to announce new and enhanced Microsoft data services: a preview of Azure HDInsight running on Linux, the general availability of Storm on HDInsight, the general availability of Azure Machine Learning, and the availability of Informatica technology on Azure.

Continue reading this blog from Microsoft

Geek guide to removing referrer spam in Google Analytics

Referrer spam occurs when your website gets fake referral traffic from spam bots and this fake traffic is recorded by your Google Analytics.

This guide tells you how to remove referrer spam (all types) from your reports by blocking the sites and IP addresses from your site so that reports are not skewed.

Wed it here on +Optimize Smart

9 Ways to Lose Your Data

Every time someone tells Brent Ozar, “This database is mission critical – we can’t have data loss or downtime,” he just smiles and shakes my head. Technology is seriously difficult.

To illustrate, here’s a collection of client stories from the last few years:

Continue reading here on Brent Ozar's blog

3 NoSQL Data Security Issues to Keep an Eye On

Serdar Yegulalp of Info World recently wrote, “Word broke last week of 40,000 instances of MongoDB that were found to be almost completely unsecured, among them a database for a French telecom with millions of customer records

Continue reading here on +DATAVERSITY