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

Why the Role of the Data Scientist is Growing

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Kevin Casey of Information Week recently wrote, “Old-school organizations will fuel the next swell of data-driven initiatives in IT. So what’s in store for the early movers and, specifically, their big-data professionals? How will the data scientist and similar roles evolve? ‘The role is becoming bigger,’ said Olly Downs, chief scientist at big-data analytics firm Globys, in a recent interview. By bigger, he means in every way — what was once a niche is now, at least in some companies, a driving force.” Read more

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 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

Yahoo7 Upgrades to Bigdata Open Source Graph Database

bigdataPenny Wolf of IT News in Australia reports, “Yahoo7 is rolling out a semantic publishing platform across the web sites that promote Channel 7’s local free to air programs, building its new content systems atop an open source graph database. Craig Penfold, CTO Yahoo7 was inspired by a similar project undertaken by the BBC, which initially launched semantic web publishing for the channel’s 2010 World Cup website to increase viewer engagement. After conducting research and discussions with BBC and Yahoo, the Yahoo7 team chose to change the datastore from a standard SQL database to a graph database.” 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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Additional Funding For Elasticsearch To Help Company Complement Its RealTime Search And Analytics Stack

elasticsearchlogoElasticsearch – whose Elasticsearch, Logstash and Kibana products for discovering and extracting insights from structured and unstructured data were discussed earlier this year here – has raised $70 million in Series C financing from New Enterprise Associates (NEA). Benchmark Capital and Index Ventures also participated in the round. That brings the total to $104 million over the past 18 months.

“Nearly all companies, start-ups and Fortune 500 enterprises alike, need to be able to slice and dice rapidly expanding data volumes in real time,” says Steven Schuurman, co-founder and CEO. The funding, Schuurman says, will be applied to enhancing sales, marketing and support personnel and efforts, as well as investing in development to build more complementary products that work with the ELK stack.

“Ultimately, this round of funding will help us get to our goal, faster, of making the ELK stack the de facto platform for businesses to gain actionable insights from their data,” he says.

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Skytree Supports Big Data Analytics in Hadoop With Hortonworks Data Platform

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SAN JOSE, CA–(Marketwired – Jun 3, 2014) - Skytree®, the Machine Learning Company®, today announced that its predictive analytics software is now available on Apache Hadoop YARN to deliver agile analytics on Hadoop clusters. Skytree’s flagship product — Skytree Server® — is built to provide high-performance Machine Learning and takes advantage of the multi-workload capabilities enabled by YARN’s increased reliability, scalability and manageability. Read more

Voice Your Opinion in the DATAVERSITY 2014 Cognitive Computing Survey

LOGO for DATAVERSITY 2014 Cognitive Computing SurveyOur sister site DATAVERSITY™ has launched the 2014 Cognitive Computing Survey which is now open to participants. The opening remarks explain, “There are 17 questions and the Survey will take approximately 15 minutes to complete. We will collect responses through Friday June 6, and send the compiled results to all those who complete the survey, on or before June 30, 2014. If you are a Consultant or Vendor, please use your most recent customer engagement to complete the survey questions.” Read more

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