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LinuxWorld’s History will Repeat Again – 100% results – Under the guidance of Mr. Vimal Daga

RedHat exams scheduled at LinuxWorld for the month of December – BootCamp would be delivered by Mr. Vimal Daga

For further inquiries or seat reservations call at our Admin Desk @ 9351009002.

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Winter Internship for B.tech Students

During the Winter Internship Program, you will get Practical Exposure on various real time case studies. We truly believe that Practical knowledge is more important than theoretical Concept that are the reason our labs our 24*7 Available.

At LinuxWorld Informatics Pvt ltd offering Practical Knowledge to students is our main Motto. All Internships are delivered by Expert Certified Trainers with Industrial Exposure.

internshipWhy LinuxWorld Winter Internship Program:

Winter Internship Program plays an important role in every student’s life and selected the best Internship company is one of the Major factors

Here are the 8 reasons you should LinuxWorld for Winter Internship

  1. Intensive Hands-on Practical Sessions.
  2. Certified Expert Trainers
  3. Authorized Internship Certificates.
  4. Project Letters.
  5. 24*7 Lab Access.
  6. Resume writing classes.
  7. Placement and career guidance.

Winter Internship Program Details:

  1. Distributed Computing using Big Data Hadoop Implementation over RedHat Linux Platform
  2. Cloud Computing Services with RedHat Linux Program
  3. OpenStack Cloud Computing Implementation Over RedHat Linux System Program
  4. Cloud Storage Implementation Over RedHat Linux System Program
  5. RedHat Linux System Administration and Engineer Program
  6. Cisco Network Administrator Program
  7. Cisco and RedHat Integrated System and Network Management Program

To more visit – http://www.linuxworldindia.org/linuxworldindia-winter-internship-industrial-training.php


DevOps benefits from data-driven private clouds

lso during our webinar, we explored how a data-driven cloud offers powerful solutions.

First, let’s define “data-driven cloud.”  A data-driven cloud is one that uses real-time, continuous analysis and measurement against totally customizable and configurable SLAs. An example is  Rackspace Private Cloud, which now includes the AppFormix cloud service optimization platform that delivers all of the game-changing benefits of a data-driven cloud.

With a data-driven cloud, operators have the ability to:

Know which parts of their infrastructure are healthy and which are not

AppFormix provides real-time monitoring of every aspect of the cloud stack, right down to the processor level. This includes visibility into every virtual and physical resource at your disposal. The user-friendly interface and customizable dashboard provide a comprehensive list of metrics based on industry best practices. SLAs are completely configurable.

Empower developers with visibility and control

AppFormix offers a dashboard that operators can share with developers via a self-service user experience. Developers then have access to process-level monitoring, with real-time and historical views of their resources and the ability to drill down to deeper and deeper levels of specificity about performance. Both operators and developers can create project-level reports with a click; the report content and the recipients are customizable, and data can be exported in any format. In addition, operators and developers have access to advanced alarming and notification capabilities and can establish static and dynamic thresholds based on their preferences.

Make well-informed capacity decisions

With AppFormix, operators know the true capacity levels of their infrastructure, any time and all the time. AppFormix also enables operators to model potential changes to see what impacts will be on capacity, availability and performance.

If this sounds great on a theoretical level, below are some “real-life” examples of what a DevOps-ready private cloud can do.

  1. Troubleshoot when a user is experiencing slowness;
  2. Real-time notification of events;
  3. Maximize infrastructure ROI using utilization reports;
  4. Determine if there is capacity for a new or expanding project;
  5. Improve availability with configurable policy for SLA.

Difference Between Data Scientist and Data Analyst

Jobs related to Data Science have topped the charts in job portals. There are job openings for various job titles like Data Scientists, Data Analysts, and Data Engineers. Though all these job titles deal with data and sound similar, they do have a number of detailed differences.  Ever wondered how different they are from each other? I did! And here are the differences I found between a Data Scientist and a Data Analyst.

Data Scientist – Rock Star of IT

A Data Scientist is a professional who understands data from a business point of view. He is in charge of making predictions to help businesses take accurate decisions. Data scientists come with a solid foundation of computer applications, modeling, statistics and math. What sets them apart is their brilliance in business coupled with great communication skills, to deal with both business and IT leaders. They are efficient in picking the right problems, which will add value to the organization after resolving it.

Harvard Business Review has named ‘Data scientist’ as the “sexiest job of the 21st century. Up-skill with Data Science now to take advantage of the career opportunities that come your way.

 

A Data Scientist can also be divided into 4 different roles based on their skill sets.

  • Data Researcher
  • Data Developers
  • Data Creatives
  • Data Businesspeople

Data Analysts – No Cool Tag Yet!

Data Analysts also plays a major role in Data Science. They perform a variety of tasks related to collecting, organizing data and obtaining statistical information out of them.  They are also responsible to present the data in the form of charts, graphs and tables and use the same to build relational databases for organizations.

A Data Analyst can also be divided into 4 different roles based on their skill sets.

  • Data Architects
  • Database Administrators
  • Analytics Engineer
  • Operations

Qualification Required for Data Scientists and Data Analysts

Qualification and Knowledge required for both Data Scientist and Data Analyst

Jobs Trends of Data Scientist and Data Analytics – As per Google Trends

Here is the trend for Data Analysts jobs as per google :

Job Trends for Data Analyst

Here is the trend for Data Scientists jobs as per google with the trend picking up mostly from 2012:

datasc

 


Major IT Players Form R Consortium to Strengthen Data Analysis

The Linux Foundation announced the formation of R Consortium, with the intention of strengthening technical and user communities around the R language, the open source programming language for statistical data analysis.

The new organization R Consortium became an official project of Linux Foundation and is designed to strengthen R language users.  It is expected that R Consortium will complement the existing fund, and will focus on expanding the user base of R, as well as focus on improving the interaction of users and developers.

The Representatives of the R Foundation and industry representatives are behind the new consortium. Microsoft and RStudio have joined the consortium as platinum members. TIBCO Software is a gold member and Alteryx, Google, HP, Mango Solutions, Ketchum Trading and Oracle have joined as silver members.

R Consortium will complement the work of R Foundation, establishing communication with user groups and engaging in supporting projects – related to the creation and maintenance of R mirror sites, testing, resources for quality control, the financial support and promotion of the language. Also, the consortium will assist in creating support packages for R and organizing other related software projects.

R is a programming language and development environment for scientific calculations and graphics that originated at the University of Auckland (New Zealand). The R language has enjoyed significant growth and now supports more than two million users. A wide grass industries adopted the R language, including biotech, finance, research and high-tech industries. The R language is integrated with frequency analysis, visualization, and reporting applications.

Having acquired the company Revolution Analytics (which makes strong use of language), Microsoft announced that it is joining the consortium together with other founding members such as Google, Oracle, HP, Tibcom, Rstudio, Alteryx to finance the new consortium.

Microsoft’s official said that “the R Consortium will complement the work of the R Foundation, a nonprofit organization that maintains the language, and will focus on user outreach and other projects designed to assist the R user and developer communities. This includes both technical and infrastructure projects such as building and maintaining mirrors for downloading R, testing, QA resources, financial support for the annual useR! Conference and promotion and support of worldwide user groups.”

Google also says they have thousands of users and their own developers using R, so this language is crucial for many of their products. Google is happy to join the rest of companies to continue to maintain the infrastructure of the open source R.

Microsoft’s support of real-time analytics for Apache Hadoop in Azure HDInsight and machine learning in Azure Marketplace use R language to service anomaly detection for preventive maintenance or detection of fraud.


3 key ways Hadoop is evolving

Hot themes at the Strata+Hadoop World conference reflect the shift for the big data platform

The Strata+Hadoop World 2015 conference in New York this week was subtitled “Make Data Work,” but given how Hadoop world’s has evolved over the past year (even over the past six months) another apt subtitle might have been “See Hadoop Change.”
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Here are three of the most significant recent trends in Hadoop, as reflected by the show’s roster of breakout sessions, vendors, and technologies.

Spark is so hot it had its own schedule track, labeled “Spark and Beyond,” with sessions on everything from using the R language with Spark to running Spark on Mesos.

Some of the enthusiasm comes from Cloudera — a big fan of Spark — and its sponsorship for the show. But Spark’s rising popularity is hard to ignore.

Spark’s importance stems from how it offers self-service data processing, by way of a common API, no matter where that data is stored. (At least half of the work done with Spark isn’t within Hadoop.) Arsalan Tavakoli-Shiraji, vice president of customer engagement for Databricks, Spark’s chief commercial proponent, spoke of how those tasked with getting business value out of data “eagerly want data, whether they’re using SQL, R, or Python, but hate calling IT.”

Rob Thomas, IBM’s vice president of product development for IBM Analytics, cited Spark as a key in the shift away from “a world of infrastructure to a world of insight.” Hadoop data lakes often become dumping grounds, he claimed, without much business value that Spark can provide.

The pitch for Hadoop is no longer about it being a data repository — that’s a given — it’s about having skilled people and powerful tools to plug into it in order to get something useful out.

Two years ago, the keynote speeches at Strata+Hadoop were all about creating a single repository for enterprise data. This time around, the words “data lake” were barely mentioned in the keynotes — and only in a derogatory tone. Talk of “citizen data scientists,” “using big data for good,” and smart decision making with data was offered instead.

What happened to the old message? It was elbowed aside by the growing realization that the culture of self-service tools for data science on Hadoop offers more real value than the ability to aggregate data from multiple sources. If the old Hadoop world was about free-form data storage, the new Hadoop world is (ostensibly) about free-form data science.

The danger s making terms like “data scientist” too generic, in the same way that “machine learning” was watered down through overly broad use.

Hadoop is become a proving ground for new tech

Few would dispute that Hadoop remains important, least of all the big names behind the major distributions. But attention and excitement seem less focused on Hadoop as a whole than on the individual pieces emerging from Hadoop’s big tent — and are put to use creating entirely new products.

Spark is the obvious example, both for what it can do and how it goes about doing it. Spark’s latest incarnation features major workarounds for issues with the JVM’s garbage collection and memory management systems, technologies that have exciting implications outside of Spark.

But other new-tech-from-Hadoop examples are surfacing: Kafka, the Hadoop message-broker system for high-speed data streams, is at the heart of products like Mesosphere Infinity and Salesforce’s IoT Cloud. If a technology can survive deployment at scale within Hadoop, the conventional wisdom goes, it’s probably a good breakthrough.

Unfortunately, because Hadoop is such a fertile breeding ground, it’s also becoming more fragmented. Efforts to provide a firmer definition of what’s inside the Hadoop tent, like the Open Data Platform Initiative, have inspired as much dissent and division as agreement and consensus. And new additions to the Hadoop toolbox risk further complicating an already dense picture. Kudu, the new Hadoop file system championed by Cloudera as a way to combine the best of HDFS and HBase, isn’t compatible with HDFS’ protocols — yet.

There’s little sign that the mix of ingredients that make up Hadoop will become any less ad hoc or variegated with time, thanks to the slew of vendors vying to deliver their own spin on the platform. But whatever becomes of Hadoop, some of its pieces have already proven they can thrive on their own


Who Is Responsible for Security in the Cloud?

Security is a primary concern for most organizations looking at cloud adoption, but who is responsible for making sure the cloud is secure? That’s one of the many questions that a Ponemon Institute survey, sponsored by security hosting vendor Armor, asked.More than half (56 percent) of respondents said that the primary reason they adopt cloud is to reduce costs, while only 8 percent said that a primary reason is to improve security, according to the study, which is based on a poll of 990 senior IT professionals in the United States and United Kingdom. Meanwhile, 79 percent of respondents indicated that security is a critical part of the cloud migration decision.”It continues to surprise me that there seems to be agreement in the industry that security is important and continues to be a major concern in the cloud,” Jeff Schilling, CSO at Armor (previously known as Firehost), told eWEEK. “However, more than half of the respondents are unwilling to pay a premium to ensure the security of their sensitive data in the cloud.”Despite the views of the survey’s respondents, it is possible to achieve a secure posture in the cloud, said Schilling, who is a former director of the U.S. Army’s Global Network Operations and Security Center, which falls under the U.S. Army’s Cyber Command.

In Schilling’s view, the cloud is the place that allows enterprises to take back the initiative from the threat actors, but it takes the right technology, managed via the right techniques and the right people. “Not investing in the proper security controls gives threat actors the advantage,” he said.

The survey asked multiple questions about responsibilities for cloud software-as-as-service (SaaS) as well as infrastructure-as-a-service (IaaS) deployments. Only 15 percent of respondents indicated that IT security is most responsible for ensuring the security of SaaS applications, while 16 percent of respondents identified IT security as most responsible for the security of IaaS resources.”Security is something that is everyone’s responsibility to some degree, yet no one particular function seems to step up and own it,” Schilling said. “This is absolutely where managed security providers can come in to take on some responsibilities and share some of the risk.”Schilling suggests that customers considering a managed service should ensure that their chosen provider clearly delineates the responsibilities that they will assume versus those that the customer will retain.The study also asked respondents about deployments of IT security technologies on-premises and in the cloud; 59 percent of respondents indicated that they deploy security information and event management (SIEM) technology on premises, while 39 percent deploy it in the cloud.”Based on my past experiences, many companies keep SIEM on premises, whether due to regulatory requirements or just by the nature of the amount of data being processed and stored,” Schilling said. “That said, we find that SIEM can absolutely work in the cloud if you have the right architecture and talent to manage it.”When it comes to intrusion-prevention systems (IPS), 54 percent of respondents noted that they deploy in the cloud, with 42 percent reporting on-premise deployments. For next-generation firewalls (NGFWs), the results are flipped, with 38 percent deploying on premises and 17 percent deploying in the cloud.”For advanced firewalls or unified threat platforms [such as a firewall-IPS combo], there is a struggle to virtualize the software and move off of bare metal,” Schilling said. “Part of me suspects this is more of a business decision by most of the vendors, as software companies drive less revenue than hardware/software companies.”The industry is starting to see some of the big players move to the cloud because they realize they will be irrelevant if they don’t have a cloud option, Schilling explained.While one part of the study showed that respondents, in fact, use security applications in the cloud, 32 percent indicated that IT security applications are considered too risky to be processed or housed in the cloud.The back-end analytics systems for some of the largest security companies in the world require tremendous horizontal and vertical scaling as their business grows and the complexity of their analytics grow exponentially, Schilling said, adding that nearly all security vendors that approach him lately have some level of public cloud use as part of their enterprises.”I love asking them to present their security validation paperwork so I can get a sense of how they are securing their cloud use,” Schilling said. “Most of the time, the conversation turns to ‘thank you for your time and I will get back to you,’ and I never hear from them.”


How a Cloud Antivirus Works

How a Cloud Antivirus Works

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panda cloud antivirus

Panda Cloud Antivirus scans your computer at regular intervals and checks it against the latest malware threats in its database.

Screenshot by Stephanie Crawford for HowStuffWorks

Whether you have years of computing behind you, or you’ve just bought your first laptop or desktop, you’re probably familiar with the need to protect computers from viruses. A virus is a software program that installs itself on your computer and makes undesirable changes to the data on your computer. Though there are rare viruses designed to target offline computers, we’re talking about malicious software (malware) you can pick up from the Internet.

To prevent malware from attacking your data, you can use antivirus software. One antivirus option is a technology called cloud antivirus. Cloud antivirus software does most of its processing elsewhere on the Internet rather than on your computer’s hard drive. Internet technology like cloud computing has made such innovations both possible and affordable.

Cloud antivirus software consists of client and Web service components working together. The client is a small program running on your local computer, which scans the system for malware. Full locally installed antivirus applications are notorious resource hogs, but cloud antivirus clients require only a small amount processing power.

The Web service behind cloud antivirus is software running on one or more servers somewhere on the Internet. The Web service handles most of the data processing so your computer doesn’t have to process and store massive amounts of virus information. At regular intervals, the client will scan your computer for any malware listed in the Web service’s database.

Here’s a summary of the advantages cloud antivirus has over traditional, locally installed antivirus software:

  • You have access to the latest data about malware within minutes of the cloud antivirus Web service learning about it. There’s no need to continually update your antivirus software to ensure you’re protected from the latest threats.
  • The cloud antivirus client is small, and it requires little processing power as you go on with your day-to-day activities online.
  • It’s free! You can get an impressive level of virus protection from the free versions of cloud antivirus software. You can also purchase upgrades for additional utilities and support, for prices that are competitive with popular local-only antivirus applications.

Now that you know what cloud antivirus is, let’s look at the features of cloud antivirus software and how you can use them to keep your system clean.


Microsoft Azure VMs Aimed at Bigger Enterprise Cloud Workloads

290x195msftlayoffs20142Microsoft is making more room on its cloud for big enterprise application workloads.In January, the company announced the general availability of high-performance G-Series virtual machines (VMs) for Azure that offered up to 32 virtual CPUs powered by cutting-edge Intel Xeon server processors, 6TB of storage capacity provided by solid-state drives (SSDs) and 448GB of memory. According to Microsoft, enterprise adoption is brisk, with a 50 percent increase in use over the past three months.Now, the Redmond, Wash.-based tech giant and cloud provider is aiming even higher.”Today, we’re excited to announce a variant of G-series, the GS-series, which combines the compute power of G-series with the performance of Premium Storage to create powerful VMs for your most storage- and compute-intensive applications,” wrote Corey Sanders, partner director of program management at Microsoft Azure, in a Sept. 2 announcement. Still powered by Intel Xeon E5 v3 processors, the new Azure VMs bring Premium Storage support into the mix.

GS-series VMs, which are compatible with both Windows and Linux, “can have up to 64TB of storage, provide 80,000 IOPS (storage I/Os per second) and deliver 2,000 [megabytes per second] of storage throughput,” Sanders said. Microsoft claims that compared to rivals, the new VMs offer more than double the disk throughput and network bandwidth (20G bps).

The new offering is aimed at large database-driven workloads, Sanders noted. “Relational databases like SQL Server and mySQL, noSQL databases like MongoDB and data warehouses can all have significant performance gains when run on GS-series,” he said.Businesses seeking to grow or enhance the performance of their existing applications can use the VMs to trade up. “You can also use GS-series to significantly scale up the performance of enterprise applications, such as Exchange and Dynamics,” Sanders added.GS-series VMs are available in five sizes. The starter size (Standard_GS1) provides two virtual CPUs, 26GB of memory, a storage performance rating of 5,000 IOPS and a maximum disk bandwidth of 125MB per second. The top-tier Standard_GS5 supports up to 32 virtual CPUs and 448GB of memory, providing the performance Sanders used to illustrate the technology’s cloud-processing potential.For businesses that don’t require quite as much cloud computing horsepower, Microsoft also announced looming price cuts for its D-Series and DS-Series VMs.”We’re continuously striving to make these more accessible at lower price points, and are pleased to announce today that we’re reducing the prices on D-series and DS-series instances by as much as 27 percent,” Sanders said. The new pricing goes into effect on Oct. 1.Azure VM customers are also getting a new diagnostic tool to aid those suffering from boot or runtime failures. The tool displays the serial and console output of running VMs.