Posts Tagged ‘Big Data’

Teradata Has Acquired Revelytix and Hadapt

teradataSAN DIEGO — Teradata (NYSE: TDC), the analytic data platforms, marketing applications, and services company, today announced two acquisitions that accelerate the growth of its big data capabilities.

On July 16th, Teradata acquired assets of Revelytix, a leader in information management products for big data with unique metadata management technology and deep expertise in integrating information across the enterprise. On July 17th, Teradata acquired assets of Hadapt, including experienced big data technologists and intellectual property.  Read more

Semantic Web Job: Big Data Architect

TekTree Systems LogoNew York’s Tektree Systems is in need of a Big Data Architect. The job description states, “Hadoop Data Architect with both hands-on Big Data and relational experience and deep knowledge of physical data modeling, data organization and storage technology, experienced with high volumes and able to architect and implement multi-tier solutions using the right technology in each tier, based on fit. Required Skills and Qualifications:

  • Design  and development of data models for a new HDFS Master Data Reservoir and one or more relational or object Current Data environments
  • Design of optimum storage allocation for the data stores in the architecture.
  • Development of data frameworks for code implementation and testing across the program
  • Knowledge and experience with RDF and other Semantic technologies
  • Participation in code reviews to assure that developed and tested code conforms with the design and architecture principles
  • QA and testing of modules/applications/interfaces.
  • End-to-End project experience through to completion and supervise turnover to Operations staff.
  • Preparation of documentation of data architecture, designs and implemented code”.

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Industry’s First Cognitive Computing App Builder

ApOrchid - VisionApp Orchid Inc recently announced the industry’s first Cognitive Computing app builder. The announcement states, “Emerging from stealth mode, AppOrchid Inc. announced today its disruptive new technology for developing cognitive apps that targets the multi-billion dollar “Internet of Everything” (IoE) market. “The future for enterprise computing lies in intelligent or cognitive apps. In this new “Internet of everything” world, connected devices, social data and massive volumes of free form documents integrate with enterprise applications in real-time. AppOrchid’s groundbreaking products employ Big Data technology and a scalable Knowledge graph model powered with intelligent natural language processing. The end result is human-like intelligence, with a gamified user experience spanning conventional, handheld and wearable devices. This is a watershed moment in Enterprise computing”, said Krishna Kumar, Founder and CEO of AppOrchid Inc.”

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Internet of Things Impacts Big Data

depiction of internet of things objectsAccording to Mike Kavis of Forbes, “Companies are jumping on the Internet of Things (IoT) bandwagon and for good reasons. McKinsey Global Institute reports that the IoT business will deliver $6.2 trillion of revenue by 2025. Many people wonder if companies are ready for this explosion of data generated for IoT? As with any new technology, security is always the first point of resistance. I agree that IoT brings a wave of new security concerns but the bigger concern is how woefully prepared most data centers are for the massive amount of data coming from all of the “things” in the near future.”

Kavis went on to write that, “Some companies are still hanging on to the belief that they can manage their own data centers better than the various cloud providers out there. This state of denial should all but go away when the influx of petabyte scale data becomes a reality for enterprises. Enterprises are going to have to ask themselves, “Do we want to be in the infrastructure business?” because that is what it will take to provide the appropriate amount of bandwidth, disk storage, and compute power to keep up with the demand for data ingestion, storage, and real-time analytics that will serve the business needs. If there ever was a use case for the cloud, the IoT and Big Data is it. Processing all of the data from the IoT is an exercise in big data that boils down to three major steps: data ingestion (harvesting data), data storage, and analytics.”

 

To read a different perspective on these challenges and how Semantic Web technologies play a role in them, read Irene Polikoff’s recent guest post, “RDF is Critical to a Successful Internet of Things.

 

Read more here.

Big Data Startup Roletroll Disrupts Job Search

roletroll

New York, NY, June 20, 2014 –(PR.com)– Former Wall St trader Adam Grealish launches Roletroll.com, a job recommendation engine for finance and tech jobs. The site uses unstructured data and statistics, collectively known as big data, to match users with jobs based on their unique skills and experiences.

 

Roletroll is a Brooklyn, NY-based start-up serving New York, Chicago and San Francisco. Job seekers upload their resumes, and computer programs do the job-search grunt work for them, aggregating jobs from multiple sources and identifying jobs that are a particularly good fit. Already Roletroll has scored over 5 million individual job matches. Read more

Big Data Challenges In Banking And Securities

Photo courtesy: Johan Hansson, https://www.flickr.com/photos/plastanka/

Photo courtesy: Johan Hansson, https://www.flickr.com/photos/plastanka/

A new report from the Securities Technology Analysis Center (STAC), Big Data Cases in Banking and Securities, looks to understand big data challenges specific to banking by studying 16 projects at 10 of the top global investment and retail banks.

According to the report, about half the cases involved e petabyte or more or data. That includes both natural language text and highly structured formats that themselves presented a great deal of variety (such as different departments using the same field for a different purpose or for the same purpose but using a different vocabulary) and therefore a challenge for integration in some cases. The analytic complexity of the workloads studied, the Intel-sponsored report notes, covered everything from basic transformations at the low end to machine learning at the high-end.

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Extracting Value from Big Data Requires Machine Learning

Involuntary Commitment

James Kobielus of InfoWorld recently wrote, “Machine-generated log data is the dark matter of the big data cosmos. It is generated at every layer, node, and component within distributed information technology ecosystems, including smartphones and Internet-of-things endpoints… Clearly, automation is key to finding insights within log data, especially as it all scales into big data territory. Automation can ensure that data collection, analytical processing, and rule- and event-driven responses to what the data reveals are executed as rapidly as the data flows. Key enablers for scalable log-analysis automation include machine-data integration middleware, business rules management systems, semantic analysis, stream computing platforms, and machine-learning algorithms.? Read more

The Skills Gap: How to Close the Gap in Your Career

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Greg Satell of Forbes recently wrote, “In the late 90’s McKinsey declared the war for talent and argued that, in a knowledge economy, having the right people is even more important than having the right strategy or technology.  Recruiting and retaining the ‘best and the brightest’ quickly became a corporate mantra. Yet today, the firm is more concerned with the skills gap.  In data science, for example it estimates a shortfall of 140,000 to 190,000 data scientists and 1.5 million managers who have the skills needed to use the insights to drive decisions. But even that understates the problem. With technology accelerating change in the marketplace and automation replacing highly skilled workers with robots, the decision to invest in any particular set of skills is far from a forgone conclusion and platitudes about ‘investing in our people’ will no longer suffice.  We need to start thinking seriously about viable strategies for managing the skills gap.” Read more

Where The Money Is: Data Science

datascigrafWhat’s a data scientist worth? Give me a second to identify the relevant data sources, build the machine learning algorithm and create a visualization.

So much for a new take on an old joke. The real answer is about six figures, information I recently came across in a report released earlier this spring: Burtch Works Executive Recruiting survey, Salaries of Data Scientists. The median base salary of data scientist managers is $160,000, it says, while individual contributors average about $120,000. The information comes from 171 data scientists for whom the recruiting firm has complete and current information. Whether a data scientist is at a lower or higher job level, across the board he or she is doing financially better than other Big Data professionals, the report shows.

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Sentiment Mining for Real Time Insights on Twitter

 

syKalev Leetaru of Wired recently wrote, “For its flagship new reality show Opposite Worlds the Syfy channel wanted to let the audience ‘remote control’ the show via social media. I worked with Syfy to create what ultimately became its real-time ‘Twitter Popularity Index.’ The Index combines the intensity of conversation around each character, the number of unique discussants, and the emotion of that discussion using a new sentiment engine powered by over 1.6 million words, phrases and common misspellings and colloquial expressions. Using our Index, Opposite Worlds records across the board in Twitter engagement for a cable television series.” Read more

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