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BlazeGraph - Open-Source Scalable Graph Database

BlazeGraph - Open-Source Scalable Graph Database | techstack | Scoop.it

SYSTAP is very pleased to launch it’s new graph database platform Blazegraph™. It is built on the same open source GPLv2 platform and maintains 100% binary and API compatibility with Bigdata®. Blazegraph™ will take over as SYSTAP’s flagship graph database. It is specifically designed to support big graphs offering both Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop, blueprints, vertex-centric) APIs. It features robust, scalable, fault-tolerant, enterprise-class storage and query and high-availability with online backup, failover and self-healing. It is in production use with enterprises such as Autodesk, EMC, Yahoo7!, and many others. Blazegraph™ provides both embedded and standalone modes of operation.

Blazegraph has a High Availability and Scale Out architecture. It provides robust support for Semantic Web (RDF/SPARQ)L and Property Graph (Tinkerpop) APIs. Highly scalable Blazegraph graph can handle 50 Billion edges on a single node.


Via Dahl Winters
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Single Page Web Applications: JavaScript End-to-End (The Hard Stuff)

In the old days, when websites were steam powered and exploded regularly, the web was simple, but slow. As it evolved it became more powerful, but harder on ...

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The Mobile Application Hacker's Handbook - PDF Free Download - Fox eBook

The Mobile Application Hacker's Handbook - PDF Free Download - Fox eBook | techstack | Scoop.it
The Mobile Application Hacker's Handbook PDF Free Download, Reviews, Read Online, ISBN: 1118958500, By Dominic Chell, Ollie Whitehouse, Shaun Colley, Tyrone Erasmus

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Mehul Mehta's curator insight, November 13, 2015 5:49 AM

This book is worth reading.

Suresh Kane's curator insight, January 5, 2016 1:01 PM

asdfasd

김지오's curator insight, September 8, 11:21 PM
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Rescooped by vitonzhang from Big Data Analytics and Science
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BlazeGraph - Open-Source Scalable Graph Database

BlazeGraph - Open-Source Scalable Graph Database | techstack | Scoop.it

SYSTAP is very pleased to launch it’s new graph database platform Blazegraph™. It is built on the same open source GPLv2 platform and maintains 100% binary and API compatibility with Bigdata®. Blazegraph™ will take over as SYSTAP’s flagship graph database. It is specifically designed to support big graphs offering both Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop, blueprints, vertex-centric) APIs. It features robust, scalable, fault-tolerant, enterprise-class storage and query and high-availability with online backup, failover and self-healing. It is in production use with enterprises such as Autodesk, EMC, Yahoo7!, and many others. Blazegraph™ provides both embedded and standalone modes of operation.

Blazegraph has a High Availability and Scale Out architecture. It provides robust support for Semantic Web (RDF/SPARQ)L and Property Graph (Tinkerpop) APIs. Highly scalable Blazegraph graph can handle 50 Billion edges on a single node.


Via Dahl Winters
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How to Become a Data Scientist

How to Become a Data Scientist | techstack | Scoop.it

Summary:  If you are wondering how to become a Data Scientist or what that title really means, try these insights.

I got started in data science way back.  I’ve been a commercial predictive modeler since 2001 and as naming trends have changed I now identify myself as a Data Scientist.  No one gave me this title.  But by observing the literature, the job listings, and my peers in the field it was clear that Data Scientist communicated most clearly what my knowledge and experience have led me to become.

These days you can get a degree in data science so you can show your diploma that certifies your credentials.  But these are relatively new so, with all due respect, if you only recently got your degree you are still a beginner.  Those of us who use this title today most likely came from combination backgrounds of business, hard science, computer science, operations research, and statistics.

What you call yourself is one thing but what your employer or client is looking for can be quite a different kettle of fish.  A lot has been written about data scientists being as elusive as unicorns.  Not being a unicorn I’d say this sets the bar pretty high.  Additionally, as I’ve perused the job listings it is equally true that the title is used so loosely and with such little understanding that an ad for data scientist may actually describe an entry level analyst and some ads for analysts are looking for polymath data scientists. 

All of this confusion over what we’re called and what we actually do can make you down right schizophrenic.  This makes it all the more complicated to answer the frequent inquiries I get from folks still in school or early in their career about how to become a data scientist.

Imagine my surprise and delight when in the space of a week two publications came across my desk that not only cast new light and understanding on this question but also have helped me understand that there is not just one definition of data scientist, but a reasoned argument (based on statistical analysis) that there are in fact four types.

Four Types of Data Scientists

The information here comes from the O’Reilly paper “Analyzing the Analyzers” by Harris, Murphy, and Vaisman, 2013.  My hat’s off to these folks for their insightful survey and conclusions drawn by statistical analysis of those results.  This is a must read.  I was able to download this at no charge from http://www.oreilly.com/data/free/analyzing-the-analyzers.csp.

There are 40 pages of good analysis here so this will be only the highest level summary.  In short, they conclude there are four types of Data Scientists differentiated not so much by the breadth of knowledge, which is similar, but their depth in specific areas and how each type prefers to interact with data science problems.

Data Businesspeople

Data Creatives

Data Developers

Data Researchers

By evaluating 22 specific skills and multi-part self-identification statements they cluster and generalize according to these descriptions.  I am betting you will recognize yourself in one of these categories.

Data Businesspeople are those that are most focused on the organization and how data projects yield profit. They were most likely to rate themselves highly as leaders and entrepreneurs, and the most likely to have reported managing an employee. They were also quite likely to have done contract or consulting work, and a substantial proportion have started a business. Although they were the least likely to have an advanced degree among respondents, they were the most likely to have an MBA. But Data Businesspeople definitely have technical skills and were particularly likely to have undergraduate Engineering degrees. And they work with real data — about 90% report at least occasionally working on gigabyte-scale problems. 

Data Creatives.  Data scientists can often tackle the entire soup-to-nuts analytics process on their own: from extracting data, to integrating and layering it, to performing statistical or other advanced analyses, to creating compelling visualizations and interpretations, to building tools to make the analysis scalable and broadly applicable. We think of Data Creatives as the broadest of data scientists, those who excel at applying a wide range of tools and technologies to a problem, or creating innovative prototypes at hackathons — the quintessential Jack of All Trades. They have substantial academic experience with about three-quarters having taught classes and presented papers. Common undergraduate degrees were in areas like Economics and Statistics. Relatively few Data Creatives have a PhD. As the group most likely to identify as a Hacker they also had the deepest Open Source experience with about half contributing to OSS projects and about half working on Open Data projects.

Data Developer.  We think of Data Developers as people focused on the technical problem of managing data — how to get it, store it, and learn from it. Our Data Developers tended to rate themselves fairly highly as Scientists, although not as highly as Data Researchers did. This makes sense particularly for those closely integrated with the Machine Learning and related academic communities. Data Developers are clearly writing code in their day-to-day work. About half have Computer Science or Computer Engineering degrees.  More Data Developers land in the Machine Learning/ Big Data skills group than other types of data scientist.

Data Researchers.  One of the interesting career paths that leads to a title like “data scientist” starts with academic research in the physical or social sciences, or in statistics. Many organizations have realized the value of deep academic training in the use of data to understand complex processes, even if their business domains may be quite different from classic scientific fields. The majority of respondents whose top Skills Group was Statistics ended up in this category. Nearly 75% of Data Researchers have published in peer-reviewed journals and over half have a PhD.

What Does this Mean for Someone Seeking to Enter the Field?

So if I am a young person seeking to enter Data Science how are these descriptions useful?  It’s possible that you could train and develop an emphasis that would lead you into the Researcher, Developer, or Creative roles.  It is less likely that education alone will put you on the Businesspeople track which implies experiences in business, not just education.  But here’s what’s interesting.  According to Harris, Murphy, and Vaisman it’s not the skills that are different but the way we choose to emphasize them in our approach to Data Science problems.  Here’s their chart.


Via Alex Kantone
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Akka vs. Finagle vs. Storm

Akka vs. Finagle vs. Storm | techstack | Scoop.it
Akka, Finagle and Storm are 3 new open source frameworks for distributed parallel and concurrent programming. They all run on the JVM and work well with Java and Scala.

Via Dahl Winters
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