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3 Big Problems Big Data Will Probably Create in Near Future

3 Big Problems Big Data Will Probably Create in Near Future

Big Data has undoubtedly been the biggest buzzword in the past one year. One can look back at the just concluded 2013 and consider it as the breakthrough year for the term Big Data.

Big_Data challengesBig Data may not be an outright term in innovation but it certainly is in awareness. In spite of the Big Data receiving more attention in the mainstream, there are business and individuals who still confuse the term and use it inappropriately.

All things said, business enterprises are investing big time in Big Data with the motive to have the best from advanced data analytics. As mobile data, internet data and cloud data trends multiply, a need for more sound Big Data adaptation platforms such as Hadoop have been felt. Though, real potential of Big Data is still very abstract to nail down, the ramifications and business challenges it will create have already begun to show from.

Let us read on for three most important problems Big Data analytics will probably create in the near future.

  1. 1.    Legal and privacy are risk issues

Big Data can be used for good, and obviously it can be harnessed for the betterment of the society. But it can also be abused! So, not everything is sunny about Big Data. Since the accumulation of data means more threat to privacy, privacy challenges around Big Data are nothing new. It may be the dark side of Big Data but an average consumer has begun to understand the implication.

This becomes challenge since enterprises use Big Data to benefit from advanced analytics. It is believed (and explained by Sand Hill survey) that almost 62 percent enterprises use Hadoop for advanced analytics it can provide.

In 2014, Big Data with the rise of Internet of Things, leading to more mobile data, drone data, sensory data and even image data is bound to create more legal concerns over Big Data privacy. This, as explained, because consumers are becoming more aware of the real impacts of Big Data on their lives. It is therefore important for enterprises to remain ahead with compliance law and keep themselves to date with changing data protection laws.

2.    Human decision making Vs. data-driven decision making

As more businesses pursue Big Data to drive their decision making, there is soon going to be a clash in ways of doing things. As MIT Sloan School of Management research scientist Andrew McAfee points out, most management education programs train employees to trust their gut. Trusting the gut feeling is the old way of decision making, so changing it with data-driven decision making can lead to conflict. Becoming data-driven will require businesses to undergo a paradigm shift, since whether the company is data driven or not will become the competitive differentiator between successful and not so successful businesses.

3.    Big Data used for discrimination

Many research projects based on the use of Big Data have raised concerns of data being used for discrimination in addition to looming privacy concerns.

Researchers including Kate Crawford of Microsoft suggest that Big Data is being used speedily for precise forms of discrimination. We are not new to discrimination, but Big Data creates a new form of automated discrimination. Researchers suggest that social media and health care are the most vulnerable.

To safeguard against the issue of discrimination, organizations can create transparent Big Data usage policies in order to protect consumer data.

 

Hadoop Can Come Handy Even When You are Not Dealing with Big Data

Hadoop Can Come Handy Even When You are Not Dealing with Big Data

Hadoop was developed to cater to the needs of web and media companies for managing big data. But even if you don’t have to deal with big data, you can still use Hadoop in many ways to enhance your data and resource management. Today Hadoop is being used by almost every business, whether they have big data or small, to manage their data.

The Main Features of Hadoop

The main feature of Hadoop is the HDFS storage system. HDFS stands for Hadoop Distributed File System that operates on low cost hardware.

MapReduce was developed for resource management and data processing but with Hadoop 2.0 it has been left just to focus on data processing while YARN is used for resource management.

These features of Hadoop can be utilized in many innovative ways by big and small businesses.

Data Archive

One straightforward use of Hadoop is to archive data files. Since HDFS runs on commodity hardware it is simple and cheap to scale so businesses can start small and expand as their business grows. They can store all their data at a very low cost.

Instead of destroying data after the regulatory period is over, companies can store decades of data and analyze it in real time to help their decision making process.

Data Staging Area

Traditionally ETL tools are used for extracting and transforming data. When Hadoop came to the scene, it could have killed ETL forever if ETL providers hadn’t been smart enough to provide HDFS connectors so that Hadoop could be used along with their ETL software.

By using Hadoop you can store the application data and the transformed data in the same place. This makes it easier to process the data at a later time and reduces the time to process the data. Hadoop can help ETL in improving data processing.

Data Processing

Instead of sending data to the warehouse and then use costly resources to update it in the warehouse, you can use Hadoop and its MapReduce function to process and update it before it goes to the warehouse. Hadoop’s low cost processing power can be used not just for your warehouse data but for other operational and analytical systems as well.

HadoopHadoop is a very powerful tool that can help all businesses to handle their data in a better way. You don’t have to be sitting on top of big data to use Hadoop. You can start even when you have small data and Hadoop will let you collect decades of data till it becomes big data and then you can start making use of all this data by using big data analytics.

New course to handle Big Data on Hadoop using R software

New course to handle Big Data on Hadoop using R software

Jigsaw Academy is introducing all new course in big data analytics using R and Hadoop. The course has been specifically designed to provide students’ knowledge and hone their skills to handle big data environment of Hadoop using the R software.

JigsawAcademyIt just been days we learnt of Cloudera and Udactiy partnership to offer open Hadoop and MapReduce courses. Course which have been specially designed to equip students with technical and analytical skills for a brighter career in emerging data market.Following the lead, Jigsaw Academy, a premier online analytics training academy, has introduced new courses in Big Data Analytics using R and Hadoop.

Jigsaw Academy has made a good name in online analytics training. It offers both intermediate and advanced level big data analytics courses. With a vision to extend its roots (as a premier academy), Jigsaw Academy has specifically designed their new course to provide everyone (in need) knowledge and help develop skills needed to deal with big data analytics on Hadoop using R software.

SaritaDigumarti, co-founder at Jigsaw Academy informs,

This new course is specifically designed for those looking to enhance their knowledge and skill sets in Big Data, specifically that of handling the big data environment of Hadoop using R software.

Who is the course for?

Since, Jigsaw Academy thrives on the continuous commitment to expand its offerings, the new course will really help global industry experts (who lack big data handling skills) garner significant expertise in the big data analytics environment. The primary target group for the course, being offered, are analytics experts who are wanting to learn and develop on their big data analytics skills.

It is also beneficial students planning to pursue a career in data science, or for those database professionals who plan to make an entry into the big data analytics industry.

Requirements for enrollment?

To attain an entry into the course, professionals and students are required to have working knowledge of R software. They should have a beginner’sunderstanding of statistics and SQL.

Those not versed with R will have to undergo a spate R skills course, which will be offered by Jigsaw Academy for free.

What to expect in and on completion of the course?

The course can be really beneficial for all the aforementioned type because the instructors at Jigsaw Academy will use real-time big data case studies. This will allows the instructors to showcase and clear the concepts of Hadoop in addition to providing training of application of big data technologies on large volume of data.

What to expect on completion?

  • A working knowledge of Hadoop
  • An ability to analyze big data using R software
  • Complete knowledge of big data analytics
  • And practical application of big data analytics

Via: PRWeb

Cloudera and Udacity partner to deliver Hadoop and Data Science training

Cloudera and Udacity partner to deliver Hadoop and Data Science training

Data education giants Cloudera and Udacity have formed a strategic partnership to address the shortage of big data skills by offering easily accessible online training for everyone. The partnership will offer open Hadoop and MapReduce Courses tailored to equip students with technical and analytical skills to have a great career in the emerging data market.

In the present scenario, as the amount of structured and unstructured data being generated and stored around the globe in various sectors has shot up considerably, there has been a significant rise in the enterprise demand for skilled and qualified workers.

Big data

Recently we read about Udacity introducing paid big data courses to bridge this widening gap of demand and supply, today we learn that Cloudera, a Apache Hadoop-powered market leader in enterprise analytic data management has partnered with Udacity, the online higher education provider, to deliver training on Hadoop and Data Science to anyone using Udacity’s easy to access online educational portal.

The course curriculum, which has be designed and developed by expert faculty at Cloudera University in collaboration with Udacity will equip the interested students with all the fundamental technical and analytical skills. The course is basically an introduction for Hadoop and MapReduce, understanding of which will help students kick start their careers in the every growing big data economy.

The course has been basically created to work as a support system for the shortage of skilled data professionals in the economy. With the course, Cloudera and Udacity are making available an open, state-of-the-art big data training within the reach of almost anyone who has access to the Internet and is passionate about learning the basics of Hadoop and MapReduce.

On completing this accessible course, students will have an opportunity to enroll in Cloudera University’s live professional training courses to earn certification for their professional training.

Via: MarketWired

11/22/20131 commentRead More
When Facebook Concluded Largest Hadoop Data Migration Ever

When Facebook Concluded Largest Hadoop Data Migration Ever

Since the inception of Facebook in particular, days of storing massive data on servers are here. Data content being shared on the internet is growing enormously with every passing day and managing the same is becoming a problem for organizations across the globe.

When Facebook Concluded Largest Hadoop Data Migration EverFacebook recently undertook the largest data migration ever.  The Facebook infrastructure team moved dozens of petabites of data to a new a center – not easy, nonetheless a task well executed.

Over the past couple of years, the amount of data stored and processed by Facebook servers has grown exponentially, increasing the need for warehouse infrastructure and superior IT architecture.

Facebook stores its data on HDFS — the Hadoop distributed file system. In 2011, Facebook had almost 60 petabytes of data on Hadoop, which posed serious power and storage shortage issues. Geeks at Facebook were then compelled to move this data to a larger data center.

Data Move

The amount of content exchanged on Facebook daily has created a demand for a large team of data infrastructure management professionals. They will analyze all the data to give it out to in the quickest and most convenient way. The treatment of such large data requires large data centers.

So considering the amount of data that had piled up, Facebook’s infrastructure team just concluded the largest data migration ever. They moved petabytes of data to a new center.

This was the largest scale data migration ever. For this Facebook set up a replication system to mirror changes from smaller cluster to the larger cluster. This allowed all the files to be transferred.

First, the infrastructure team used the replication clusters to copy and transfer bulk data from the source to the destination cluster. Then the smaller files, Hive objects and user directories were copied onto the new server.

The process was complex, but since the replication clusters minimize downtime (time how quickly both old and new clusters can be brought to identical state), it became easy to transfer data on a large scale without a glitch.

Learning curve

According to Facebook, the infrastructure team has used a replication system like this one previously too. But, earlier, the clusters were smaller and could not accommodate the rate of data creation, which meant these clusters weren’t enough.

The team worked day in and day out for the data transfer. With the use of the replication approach, the migration of data became a seamless process.

Now, the team having transferred massive data to a bigger cluster means that Facebook can deliver absolutely relevant data to all users.

All Are Valuable Members of Hadoop Community says Cloudera CEO

All Are Valuable Members of Hadoop Community says Cloudera CEO

Within three months of his taking over the leadership of the company, CEO Cloudera Tom Reilly has already visualized where the company is headed.

Cloudera CEO Tom ReillyAccording to him, one needs to have a strong and far-sighted vision for the company if it is to compete against the likes of Hortonworks and MapR for a share of the pie in the highly evolving Hadoop market.

Despite the tough competition, Reilly remains a well wisher for his rivals, whom he views as valuable members of the Hardoop community. His message to his employees is also the same – consider all your competitors as valuable contributors for the success of the community.

Interestingly, Reilly credits Hortonworks, one of the fellow startups and rivals, for driving the development of YARN, which has provided the much needed impetus to every major player in Hardoop.

He also affirmed that the real competition his company faces is from information giants such as Pivotal and IBM, and not other start up rivals.

Cloudera’s CEO was a little shy in sharing details about the change of focus of his company. He has kept the details safe for a public announcement during the Hadoop World conference to be held next week.

Nevertheless, industry watchers estimate that Reilly’s plans for Cloudera are bigger than before. He doesn’t want the company to become just another Hadoop distribution company. With an ever growing list of features and over 700 partners, he aims to make it a data giant that delivers real value to enterprises.

When confronted on the question of Hortonworks luring away Spotify from Cloudera, Reilly has altogether a different take. He confesses that the development surely hurt from a public relations perspective, but it’s not something that will pull the company’s shoulders down.

All Are Valuable Members of Hadoop Community says Cloudera CEO

He explained that Spotify wanted a comprehensive enterprise support and was no longer interested in making use of the free version of Cloudera’s software. Along with Hortonworks, his company too listed a price for the deal. However, Hortonworks managed to put up a slightly better contract and Cloudera didn’t try to match it intentionally, claiming it didn’t make good business sense for them.

Reilly appears too outlandish when he states that the deal didn’t matter much to them and Spotify only managed to earn a low priced vendor with this contract. But deep within, Reilly knows that in order to make his company ride towards profitability, he needs to turn out better than his competitors.

Experts believe that even though Cloudera has a lot on its platter, the 800 pound Hardoop startup can’t distance itself from the present competition.

Unless the company takes a big leap to stand in line with information giants, it will have to live with the image of a Hardoop startup.

The Demand for Hadoop & NoSQL Skills Goes Up

The Demand for Hadoop & NoSQL Skills Goes Up

Every since organizations have begun using Big Data to their advantage, a demand for data analytic specialists or Data scientists has grown manifold. Increase in demand for big data experts means an automatic increase in demand for experts with Hadoop and NoSQL skills.

A rise in big data has compelled companies and organizations both big and small to desperately start looking out of IT professionals who can help them in maintaining and monitoring their database for them.

Big data market is still in very early phase, it has a long way to go, but businesses have realized that there is no future if they do not manage this large data adequately. Therefore, a demand for database management skills has increased to many industries beyond web and software, where it started. Today industries like retail, healthcare and even government are seeking professionals with skills to manage and analyze the large data sets for them.

The Demand Hadoop & NoSQL Skills Goes Up

When we talk about big data experts, one of the most desired skills is NoSQL and Hadoop knowledge. An individual cannot be a data expert without thorough knowledge of Hadoop and NoSQL. Data experts have become a professional really in demand, and knowledge of Hadoop and NoSQL adds to the prowess of an individual who can earn highly competitive salaries with data expertise.

Thanks to the companies like Amazon, Apple etc that are looking for big data experts, there has been a significant jump in salaries of data experts and the profession has suddenly become a dream job for many.

Some careers that need NoSQL and Hadoop skills

Some of the careers where NoSQL and Hadoop skills are being put to good use include:

Data Scientist: Data scientist or big data analytic specialist is a profession that requires a person to have a variety of data driven skills. Data scientists gather, analyze, present, and predict the data. Currently given the size of data ever increasing, data scientists are highly in demand.

Data Architect: Data architects are professionals who create data models, analyze data and assist in data warehousing and migration. To be a Data architect an individual required DBA and Hadoop skills.

DBA: DBA or Database Administrator is a career that is massively in demand lately. Companies that hire DBAs look for professionals with skill sets to handle platforms like Oracle, MongoDB etc. The more familiar an individual is with the NoSQL and Hadoop skills the better package he/she can seek.

Strata + Hadoop World 2013 an event for big data junkies

Strata + Hadoop World 2013 an event for big data junkies

The Strata + Hadoop World will see some of the most influential decision makers, developers, analysts and architects of big data come together to streamline the future of business and technology. Anyone who wishes to tap into the opportunities foresighted by big data needs to be at the Strata + Hadoop World, since the event is one of the biggest gatherings of Hadoop community anywhere in the world.

Strata + Hadoop World 2013

The future definitely belongs to companies and organizations that learn how to manage the influx of large data to their advantage. And to be able to understand the significance of big data and how it can be streamlined to one’s advantage it becomes really important to make an appearance at the Strata + Hadoop World.

The great event is part of NYC DataWeek celebration, which explores people and organizations dealing with big data to fuel innovation in the city of New York. NYC DataWeek invites all people to attend data related events, most of which are open and free to attend.

Why you should attend the event

  • It is an opportunity to understand the advantages and challenges of big data
  • Find new and innovative ways to channelize data assets
  • Understand and learn how to use data from science projects to your advantage in business application
  • Know about the career opportunities for data scientists (professionals) and how they can be hired or what training is necessary
  • Meet in person with people from same walks of life and learn from their data managing skills

The popularity of the event is such that with five odd days to go, the Strata + Hadoop World 2013 is completely sold out. The event is going to be a great affair, so you must not miss it. In case you cannot attend the event in person, you can participate in either of the following ways:

  • Follow @strataconf on Twitter for news and updates
  • Watch it live, including keynotes and interviews beginning October 29
  • Take home video compilation of Strata + Hadoop World 2013. It will be complete with keynotes, sessions, interviews and tutorials etc.
Hadoop – A Brief Introduction

Hadoop – A Brief Introduction

Earlier, it used to be a tough job to store enormous data sets on distributed server clusters. With technological advancements that poured in over the last two decades, however, it has become feasible to both store and analyze big chunks of data without having to shell out hefty budgets.

What is Hadoop and How does it work

One of the amazing techniques that enable easy storage of massive data sets and helps run distributed analysis applications in each cluster unit is known as Hadoop. It IS a big deal in big data and many experts recognize it as a major force.

Let’s get down to the basics.

What is Hadoop?

Basically, Hadoop is an open source software platform. It was introduced by the Apache Software Foundation. It is a simple yet effective technological solution that turned out to be highly useful in managing huge data, a mixture of structured and complex data in particular, quite efficiently and cheaply.

Hadoop has been specially designed to be strong enough to help big data applications run smoothly despite the failure of individual servers. This software platform is highly efficient and does not require applications to transport big data volumes across the network.

How does it Work?

Hadoop software library can be described as a framework which uses simple programming models to facilitate the distributed processing of huge data sets through clusters of computers. The library is not dependent on hardware for high-availability because it can find out and handle failure in the application layer itself. In a way, it delivers readily available services on top of a server of computers that are prone to failure.

Since Hadoop is fully modular, it allows you to swap out nearly all its machineries for a totally different software tool. The architecture is stout, flexible and efficient.

What are Hadoop Distributed File Systems? 

A distributed file-system for the storage of data and a data processing framework are two main parts of Hadoop. These two components play the most important role.

Technically, the distributed file-system is a compilation of storage clusters holding the actual data. Although Hadoop can use different file systems, it prefers to use Hadoop Distributed File Systems (which are cleverly named) for security reasons. Once placed in HDFS, your data stays right there until some operations are required to be performed on it. You can run an analysis on your data or export it to another tool right there within Hadoop.

Hadoop – Data Processing Framework

MapReduce is the default name of the java-based system that works as the data processing framework. We hear more about MapReduce as compared to HDFS  because it is the very tool that actually processes data and is a wonderful platform to work with.

Unlike a regular database, Hadoop does NOT involve queries, SQL (structured query language) or otherwise. Instead, it simply stores data that can be pulled out of it when required. It is a data warehousing system that simply needs a mechanism such as MapReduce for data processing.