31 March 2015

Hadoop Skills Free Video Training

Are you interested in the world of Big data technologies, but find it a little cryptic and see the whole thing as a big puzzle. The hadoop free video training really useful to learn quickly.

Are you looking to understand how Big Data impact large and small business and people like you and me?
Do you feel many people talk about Big Data and Hadoop, and even do not know the basics like history of Hadoop, major players and vendors of Hadoop. Then this is the course just for you!
This course builds a essential fundamental understanding of Big Data problems and Hadoop as a solution. This course takes you through:
  1. Understanding of Big Data problems with easy to understand examples.
  2. History and advent of Hadoop right from when Hadoop wasn’t even named Hadoop.
  3. What is Hadoop Magic which makes it so unique and powerful.
  4. Understanding the difference between Data science and data engineering, which is one of the big confusions in selecting a carrier or understanding a job role.
  5. And most importantly, demystifying Hadoop vendors like Cloudera, MapR and Hortonworks by understanding about them.
What are the requirements
  • Interest in new technical field of Big Data
  • Interest in a new technology: Hadoop.
  • What am I going to get from this course?
  • Over 8 lectures and 44 mins of content!
  • To build fundamental knowledge of Big Data and Hadoop
  • To build essential understanding about Big Data and Hadoop.
What is the target audience
  • Big Data and Hadoop Enthusiast
  • Non-geeks and any one who wants to know about Big Data.

References

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27 March 2015

Trending IT Skills - Grab Opportunities

Great demand for Mobile developers and Security professionals.

Read my other article on "15 Rapidly Growing IT Jobs". Growing areas are in Internet of things and 3D printing.

Then we need to know what is 3D printing?

3D printing is not necessarily transforming IT. It’s that it's a new technology that's transforming other industries like manufacturing and biomedical sectors," Laura McGarrity, vice president of digital marketing strategy for Mondo, told eWEEK. "It does create new job growth within the IT industry. Benefits to consumers include the ability to try and re-work prototypes before going to the market - consumers can expect products made for their specific needs.

An excellent growth for Data related and Statistics jobs. Data and statistics are everywhere now, and there is high demand for scientists who can analyze the data," she said. "A data scientist needs to be able to manage data, create predictions, and communicate the results.

What trend IT professionals need to follow?


The only mantra, IT professionals need to follow is "Be open to learning at all times and working in agile teams and work cultures," she said. "Educate yourself by networking and attending meet-ups and code camps

15 March 2015

Poor Data Quality-New Job Roles in Data Quality

Data quality is on rise and important to the organizations today. Since in Experian research it has found that poor data quality causing losses to the companies.

Experian research suggests companies in the UK, the US, Australia and western Europe have poorer quality data this year than last. The credit information company’s 2015 Global Data Quality Research among 1,239 organisations found a dramatic lack of data quality “ownership”, and 29% of respondents were still cleaning their data by hand.
 
The number of organisations that suspect inaccurate data has jumped from 86% in 2014 to 92%. Also, respondents reckoned 26% of their data to be wrong, up from 22% in 2014 and 17% in 2013. Some 23% of respondents said this meant lost sales, up from 19% in 2013.
Boris Huard, managing director of Experian Data Quality, said: “Getting your data strategy right is vital if you want to be successful in this consumer-driven, digitalised age. It is encouraging that companies are increasingly switching on to the value of their data assets, with 95% of respondents stating that they feel driven to use their data to understand customer needs, find new customers or increase the value of each customer.”

Poor Data Quality is cost million pounds to the companies.About one-third of organisations use automated systems, such as monitoring and audit technology (34%), data profiling (32%) or matching and linkage technology (31%) to clean their data. A total of 29% still use manual checking to clean their data.

Huard added: “As our Dawn of the CDO research demonstrated, a new breed of chief data officers, chief digital officers and director of insights are emerging – new roles that have come about in response to the pressure and opportunity presented by big data.”

However, only 35% of respondents said they manage data quality by way of a single director and nearly 63% are missing a coherent, centralised approach to data quality. More than half said individual departments still go their own way with respect to data quality enforcement, and 12% described their data quality efforts as “ad hoc”.

13 March 2015

The story Hadoop data value less in cost than ETL

Traditional data warehouse

That isn’t to say that Hadoop can’t be used for structured data that is readily available in a raw format; because it can.In addition, when you consider where data should be stored, you need to understand how data is stored today and what features characterize your persistence options. 
  • Consider your experience with storing data in a traditional data warehouse. Typically, this data goes through a lot of rigor to make it into the warehouse.
  •  Builders and consumers of warehouses have it etched in their minds that the data they are looking at in their warehouses must shine with respect to quality; subsequently, it’s cleaned up via cleansing, enrichment, matching, glossary, metadata, master data management, modeling, and other services before it’s ready for analysis. 
  • Obviously, this can be an expensive process. Because of that expense, it’s clear that the data that lands in the warehouse is deemed not just of high value, but it has a broad purpose: it’s going to go places and will be used in reports and dashboards where the accuracy of that data is key. 
Big data in Hadoop

Big Data repositories rarely undergo (at least initially) the full quality control rigors of data being injected into a warehouse, because not only is prepping data for some of the newer analytic methods characterized by Hadoop use cases cost prohibitive (which we talk about in the next chapter), but the data isn’t likely to be distributed like data warehouse data. We could say that data warehouse data is trusted enough to be “public,” while Hadoop data isn’t as trusted (public can mean vastly distributed within the company and not for external consumption), and although this will likely change in the future, today this is something that experience suggests characterizes these repositories.

Specific pieces of data have been stored based on their perceived value, and therefore any information beyond those pre-selected pieces is unavailable. This is in contrast to a Hadoop-based repository scheme where the entire business entity is likely to be stored and the fidelity of the Tweet, transaction, Facebook post, and more is kept intact. 

Data in Hadoop might seem of low value today, or its value nonquantified, but it can in fact be the key to questions yet unasked. IT departments pick and choose high-valued data and put it through rigorous cleansing and transformation processes because they know that data has a high known value per byte (a relative phrase, of course).

ETL and Big data
Stockphotos.io

Why else would a company put that data through so many quality control processes? 

Of course, since the value per byte is high, the business is willing to store it on relatively higher cost infrastructure to enable that interactive, often public, navigation with the end user communities, and the CIO is willing to invest in cleansing the data to increase its value per byte.
  • With Big Data, you should consider looking at this problem from the opposite view: With all the volume and velocity of today’s data, there’s just no way that you can afford to spend the time and resources required to cleanse and document every piece of data properly, because it’s just not going to be economical. 

What’s more, how do you know if this Big Data is even valuable? 

Are you going to go to your CIO and ask her to increase her capital expenditure (CAPEX) and operational expenditure (OPEX) costs by fourfold to quadruple the size of your warehouse on a hunch? 

For this reason, we like to characterize the initial nonanalyzed raw Big Data as having a low value per byte, and, therefore, until it’s proven otherwise, you can’t afford to take the path to the warehouse; however, given the vast amount of data, the potential for great insight (and therefore greater competitive advantage in your own market) is quite high if you can analyze all of that data.
  • The idea of cost per compute, which follows the same pattern as the value per byte ratio. If you consider the focus on the quality data in traditional systems we outlined earlier, you can conclude that the cost per compute in a traditional data warehouse is relatively high (which is fine, because it’s a proven and known higher value per byte), versus the cost of Hadoop, which is low.
Of course, other factors can indicate that certain data might be of high value yet never make its way into the warehouse, or there’s a desire for it to make its way out of the warehouse into a lower cost platform; either way, you might need to cleanse some of that data in Hadoop, and IBM can do that (a key differentiator). 

For example, unstructured data can’t be easily stored in a warehouse.

Indeed, some warehouses are built with a predefined corpus of questions in mind. Although such a warehouse provides some degree of freedom for query and mining, it could be that it’s constrained by what is in the schema (most unstructured data isn’t found here) and often by a performance envelope that can be a functional/operational hard limit. Again, as we’ll reiterate often in this book, we are not saying a Hadoop platform such as IBM InfoSphere BigInsights is a replacement for your warehouse; instead, it’s a complement.
  • A Big Data platform lets you store all of the data in its native business object format and get value out of it through massive parallelism on readily available components. For your interactive navigational needs, you’ll continue to pick and choose sources and cleanse that data and keep it in warehouses. But you can get more value out of analyzing more data (that may even initially seem unrelated) in order to paint a more robust picture of the issue at hand. 
Indeed, data might sit in Hadoop for a while, and when you discover its value, it might migrate its way into the warehouse when its value is proven and sustainable.

10 March 2015

Distributed Computing - New Trends

Distributed information systems are becoming more popular as a result of improvements in computer hardware and software, and there is a commensurate rise in the use of the associated technologies. Because of the increasing desire for business-to-business (B2B) communication and integration, technologies such as Service-Oriented Computing (SOC), Semantic Web, Grid, Agents/Multi-agents, peer-to-peer, etc., are receiving a high level of interest nowadays.

As a part of distributed information systems, web information systems play an important role in the modern, ubiquitous Internet world and the applicability of Web Services as a particular implementation of SOC has been widely recognized for current B2B integration (e.g. e-commerce, e-government and e-healthcare).

However, building all aspects of Web Services comprehensively needs further improvement, for instance, Quality of Service (QoS) has yet to be properly addressed. Likewise, the detection of service availability to achieve self-healing in the invocation process, service reuse, how best to define atomic services, and service composition are all issues that urgently require more research.

Meanwhile, it should be noted that Web Services play only a partial role in evolving distributed information systems. With the development of future computer hardware, software and business requirements, many other technologies will probably emerge that will serve particular business goals better. Therefore, much recent research has been focusing not only on individual technologies in distributed systems, but also on the possibility of combining currently available technologies to improve business outcomes.

We concentrate mainly on Web Services and technical issues associated with current Web Services standards, but we also give a brief overview of three other distributed technologies, namely Grid, agents and Semantic Web, which can work with Web Services. Thus, it concentrates initially on the background of services in distributed information systems, then it introduces Grid, agent and Semantic Web technologies.

After that, it discusses several technical aspects of Web Services in current distributed information systems, in particular, general Web Service availability and performance issues and the possibility of combining agent technology and Web Services to provide improved understanding of service availability. We then introduce JSON (JavaScript Object Notation), which may provide an alternative to current approaches that will deliver better Web Service Performance and discuss service composition, illustrating it with an implementation from the EU Living Human Digital Library (LHDL) project

Read my next post for part-2

04 March 2015

APPIAN, PEGA, Oracle BPM Tools-Career Options

Gartner recently broadened the definition of BPM, recasting it as "a management practice that provides for governance of a business's process environment toward the goal of improving agility and operational performance."¹ This more holistic view offers a structured approach for optimizing processes and takes into account the software tools discussed above as well as an organization's methods, policies, metrics, and management practices. 
According to Gartner, BPM is about becoming a process-managed organization, which requires the following disciplines (in addition to Information Technology):

BPM Career Options
BPM Career Options
Appian BPM is a business process management solutionthat can help users to manage work automations, business processes and social collaboration across the enterprise for customers, employees and systems.

General Skills required as BPM developer:

Functional knowledge of: 

  • BPMN
  • JavaScript
  • SQL
  • HTML
  • CSS
  • SOAP
  • Web services standards
  • XML
  • XPATH
  • XSLT
  • LDAP/AD

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