Big Data Predictions for 2014

Big Data Predictions for 2014

Big data was seen as one of the biggest buzzwords of 2013 and organizations are using a great deal on Big data analytics.  The storage and analysis of large and/or complex data sets utilizing an arrangement of procedures including, not restricted to: Nosql, Mapreduce and machine learning. Organizations are executing big data activities to enhance operational choice making over the undertaking.

In 2014, data analysts will be engaged through simple to-utilize device that influence the bits of knowledge of data researchers, by giving constant gauges and suggestions in their normal business apparatuses. Better investigation will make data dissection more successful, while computerization arranges for data researchers to concentrate on key activities and opening further esteem in corporate data saves. Here are 10 Big Data Predictions for 2014.

1. Big data-as-a-service will become a big deal
Regardless of cases from analysts that all organizations will look to contract data researchers, this simply isn't going to happen. Firstly, there's a deficit of data researchers, which goes somehow in clarifying why organizations are retraining existing staff to work with big data) and furthermore, not all organizations are prepared to (nor do they have to) put resources into full-time data researchers to investigate and demonstrate their data.

Rather, in the same way that in different ranges, I want a wave of organizations hustling to enter the big data-as-an administration space, a thought that started to crawl into the last parts of 2013. This could be anything from little and medium organizations joining to anything from whole bundles of putting away, examining, clarifying and picturing data to more conservative administrations, which concentrate on exchanging data to cloud-based servers to consider an open method for addressing the data later on.

2. Hadoop is an open-source software

Hadoop, famously named after a toy elephant, is a well-known piece of software to anyone curious about data science and it provides the backbone for many big data systems, allowing businesses to store and analyse masses of data. Most importantly, it’s open source, which means that its implementation was inexpensive, allowing many organisations to understand, rather than ignore, the data they were collecting.

3.Big data innovation in the open-source community

“New open-source projects like Hadoop 2.0 and YARN, as the next-generation Hadoop resource manager, will make the Hadoop infrastructure more interactive. New open-source projects like STORM, a streaming communications protocol, will enable more real-time, on-demand blending of information in the big data ecosystem,” wrote Quentin Gallivan, CEO of business analytics software firm Pentaho,

4.From Big Data to Extreme Data

The volume, velocity and variety of data will continue to grow exponentially in 2014, simpler analytics tools will be needed to leverage the “data deluge.”

“It’s more than the three Vs—volume, velocity, and variety—that make big data such a difficult tiger to tame,” the IEEE says. “It’s that the technology world hasn’t quite caught up with the need for trained data scientists and the demand for easy-to-use tools that can give industries—from financial and insurance companies to marketing, healthcare, and scientific research organizations—the capability to put the data they gather into meaningful perspective. The current era of extreme data requires new paradigms and practices in data management and analytics, and in 2014 the race will be on to establish leaders in the space.”

5. 2014 will be the year of SQL on Hadoop

“I think you’ll see people start building interactive applications on the Hadoop infrastructure. And what I mean by that — and I think this is probably the most controversial thing — is that people will start replacing their first-generation relational databases with SQL on Hadoop,” said Monte Zweben, CEO of SQL-on-Hadoop database startup Splice Machine

Hadoop Introduction - Hadoop Training Bangalore

What Is Apache Hadoop?

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.

Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.

Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabytes. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating uninterrupted in case of a node failure. This approach lowers the risk of catastrophic system failure, even if a significant number of nodes become inoperative.

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Big Data Opportunities and Career

Big Data Opportunities and Career

Intimidating or not, Big Data is a natural result of our collective obsession with technology. Put simply, it is a reference to mountainous volumes of data that can be amassed by companies or even individuals for that matter. Around a decade ago, it was only the scientific community that actually had what took to wade through reams of data. Today, the need for this kind of diligence is something that is faced by almost every stream.

We do have software that can handle large amounts of data; however, Big Data is now representative of massive data sets which cannot be handled by commonly used ones. This may sound strange, but Big Data now refers to a single dataset size, ranging from few dozen terabytes to petabytes.

A recent report  found that in 15 of the U.S. economy’s 17 sectors, companies with more than 1,000 employees store, on average, over 235 terabytes of data, more data than is contained in the U.S. Library of Congress.

Though Big Data may sound futuristic, it does need certain exceptional technologies to efficiently process huge volumes of data in a good span of time. Here are some of the technologies that can be applied to the handling of big data.

Massively Parallel Processing (MPP) – This involves a coordinated processing of a program by multiple processors (200 or more in number). Each of the processors makes use of its own operating system and memory and works on different parts of the program. Each part communicates via messaging interface. An MPP system is also known as “loosely coupled” or “shared nothing” system.

Distributed file system or network file system allows client nodes to access files through a computer network. This way a number of users working on multiple machines will be able to share files and storage resources. The client nodes will not be able to access the block storage but can interact through a network protocol. This enables a restricted access to the file system depending on the access lists or capabilities on both servers and clients which is again dependent on the protocol.

Apache Hadoop is key technology used to handle big data, its analytics and stream computing. Apache Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of commodity servers. It can be scaled up from a single server to thousands of machines and with a very high degree of fault tolerance. Instead of relying on high-end hardware, the resiliency of these clusters comes from the software’s ability to detect and handle failures at the application layer.

Data Intensive Computing is a class of parallel computing application which uses a data parallel approach to process big data. This works based on the principle of collocation of data and programs or algorithms used to perform computation. Parallel and distributed system of inter-connected stand alone computers that work together as a single integrated computing resource is used to process / analyze big data.

According to IBM, 80 per cent of world’s data is unstructured and most businesses don’t even attempt to use this data to their advantage. Once the technologies to analyze big data reach their peak, it will become easier for companies to analyze massive datasets, identify patterns and then strategically plan their moves based on consumer requirements that identified through historic data.

BigData Training - Chennai [ Apache Hadoop]: Hadoop Best Practise

BigData Training - Chennai [ Apache Hadoop]: Hadoop Best Practise: Hadoop is a free Java framework for distributed applications and intensive data management. It enables applications to work with thousands...

Hadoop Best Practise

Hadoop is a free Java framework for distributed applications and intensive data management. It enables applications to work with thousands of nodes and petabytes of data
 
With Hadoop, companies discover and put into practice new techniques for analysis and retrieval of data, techniques previously impossible to implement for reasons of performance, cost and technology. As a result, Hadoop is an option that is gaining popularity to treat, store and analyze large volumes of raw data, semi-structured or unstructured data from the most disparate data sources.
 
More than one billion users of Facebook generated about 2,500 million updates, about 300 million images and links. This makes the social network to process more than 500 terabytes of content daily.
 
Hadoop is used in three different ways: as a data warehouse for web analytics, to store distributed database, and finally for backups of these MySQL database servers.
In addition, Hadoop is used for thousands of simultaneous requests, mining operations, social analysis, and management of resources.
The main strength Hadoop lies in its scalability. 

BigData Training - Chennai [ Apache Hadoop]: Big Data: A new competitive advantage

BigData Training - Chennai [ Apache Hadoop]: Big Data: A new competitive advantage: The utilization of Big Data is turning into a vital path for heading organizations to beat their companions. In most businesses, secured c...

Big Data: A new competitive advantage

The utilization of Big Data is turning into a vital path for heading organizations to beat their companions. In most businesses, secured contenders and new participants apparently equivalent will influence data driven techniques to improve, contend, and catch esteem.

In healthcare, data pioneers are analyzing the health outcomes of pharmaceuticals when they were widely prescribed, and discovering benefits and risks that were not evident during necessarily more limited clinical trials. Other early adopters of Big Data are using data from sensors embedded in products from children’s toys to industrial goods to determine how these products are actually used in the real world. Such knowledge then informs the creation of  new service offerings and the design of future products

Big Data will help to create new growth opportunities and entirely new categories of companies, such as those that aggregate and analyse industry data. Many of these will be companies that sit in the middle of large information flows where data about products and services, buyers and suppliers, consumer preferences and intent can be captured and analysed. Forward-thinking leaders across sectors should begin aggressively to build their organisations’ Big Data capabilities.

In addition to the sheer scale of Big Data, the real-time and high-frequency nature of the data are also important. For example, ‘nowcasting,’ the ability to estimate metrics such as consumer confidence, immediately, something which previously could only be done retrospectively, is becoming more extensively used, adding considerable power to prediction. Similarly, the high frequency of data allows users to test theories in near real-time and to a level never before possible.

BigData Training - Chennai [ Apache Hadoop]: Careers in Big Data & Hadoop

BigData Training - Chennai [ Apache Hadoop]: Careers in Big Data & Hadoop:  There are more j ob opportunities in big data management and analytics than there were last year and many IT professionals are prepa...

Careers in Big Data & Hadoop

 There are more job opportunities in big data management and analytics than there were last year and many IT professionals are prepared to foot the bill for training, according to research.

Some 48% of respondents said they are seeing more job opportunities in big data and 63% are willing to invest their own time or money to acquire these skills (63%).

"To meet the demand for professionals, technology recruiters need to ensure they learn about big data. With job vacancies in analytics and management rising, but knowledge faltering, it’s vital that recruiters understand the requirement of companies to place the right professionals within them.”

“New technologies are driving big data adoption and creating a greater need for professionals skilled in managing this information. Despite predictions that by 2015 big data could create around 4.4 million jobs, only one-third of these positions will be filled,” said Woodman.

“Large data sets come in many forms, and new technologies are now available to ingest, store, process and visualize that data in real time. With the data readily available, companies need to react now to ensure that they have sufficient levels of skilled professionals who can analyse and manage the information, to ensure it is of the greatest benefit to businesses.”

BigData Training - Chennai [ Apache Hadoop]: Five ways Big Data is revolutionizing retail marke...

BigData Training - Chennai [ Apache Hadoop]: Five ways Big Data is revolutionizing retail marke...: Five ways Big Data is revolutionizing retail marketing 1. Growing, cross-channel data volumes The rise of mobile, tablets and social me...

Five ways Big Data is revolutionizing retail marketing

Five ways Big Data is revolutionizing retail marketing

1. Growing, cross-channel data volumes

The rise of mobile, tablets and social media has accelerated the growth of available customer data. A typical retailer knows not only the basic demographic information about a customer, but purchase history, call center interaction, mobile/social interaction, supply chain data and more. The sheer volume of information available to retailers is unprecedented, even for brands that have years of experience analyzing customer data.

2. Increasing investment in technology

You’d be hard-pressed to walk into a Best Buy right now and find a hard drive that stores less than a terabyte. Storage is so cheap that it’s leveling the playing field for many companies when it comes to Big Data. Retail leaders have started investing in centralized databases and focusing on data hygiene and analytics, giving them insight into their customers that wasn't possible even a few years ago.

In 2013, retailers will spend nearly $2 billion on business intelligence and $9.4 billion on infrastructure. For Macy’s, the investment has paid off: Tomak, the executive who modernized Macy’s data processes, attributes a 10 percent increase in store sales to improved analytics capabilities.

3. Solving the omnichannel puzzle with data

Retailers with a data-centric mindset are crunching an incredible amount of customer behavior data to understand how customers are researching and buying products. Insights reached through analyzing transaction data, foot traffic and in-store checkout wait times have led to shifts in marketing strategies and in-store tactics. In response, retailers have introduced in-store kiosks, free Wi-Fi, and armed their sales staff with mobile devices that allow them to better serve Web-savvy customers on the spot.

Similarly, marketers shouldn’t ignore one channel at the expense of the other. In fact, Walgreens found that customers who shop both in-store and online spend 3.5 times as much as customers who favor only one channel.

4. Improving personalization

Big Data gives retailers the unique opportunity to mirror the shopkeeper of yore, adapting communication and sales techniques to life events and preferences. Research cited on the Harvard Business Review blog found that personalization can deliver five to eight times the ROI on marketing investment and boost sales 10 percent. Consumers are fine with sharing personal details so long as it earns them something.

Technology will further enhance the consumer experience as Next Best Offer (NBO) technology becomes reality. NBO represents the convergence of real-time data analysis and mobile offers. By reaching consumers at the right time, in the right place, through the right channel, NBO provides personalization on steroids and is the future of the industry.

5. Segmenting the most valuable customers

Harnessing Big Data is a massive undertaking, but the payoff lies in finding the most profitable customers. Prioritizing these high-value customers is essential to success, especially considering that it costs more to acquire new customers than to keep the best customers. Improvements in data-crunching abilities allow retailers to analyze the behavior and needs that drive individual customers, which results in more relevant and targeted offers.

A recent study by Aberdeen Group found that 59 percent of retailers identified a lack of consumer insights as their top data-related pain point. Yet retailers have more customer data than ever. For brands competing in an industry with slim margins, harnessing the right data and smart analysis will lead to better engagement, more loyal customers and a competitive advantage.

Maximizing Big Data’s potential

Retailers that are taking advantage of Big Data’s potential are reaping the rewards.  They’re able to use data to effectively reach consumers through the correct channels and with messages that resonate to a highly targeted audience.  Although there are obvious benefits, many retailers are surprisingly still failing to act on these trends. This delay is largely due to a dependence on siloed information, lack of executive involvement and a general trend among marketers to fail to understand analytics. Without advancing internal structures, gaining executive support or educating internally, jumping on these Big Data trends is nearly impossible.

Big Data can be overwhelming, and it’s important that retailers understand what their current systems can handle. For data to produce results, retailers need to integrate technology to ensure that they are gaining insights they can quickly act upon. Once internal resources are up to date – including both human knowledge and technology assets – Big Data possibilities are limitless.