Why Is Big Data A Big Deal For Ebay?

Almost all internet based businesses today are designed and developed with the use of distributed object technology which requires security, high availability, high performance and scalability. It is also required to be able to handle very large amount of requests forwarded by its internet community. It must also be capable of responding to all of these requests adequately and in very timely fashion. It’s not possible for any customer to be able to go through lacs of products available on its site (WeForum, 2015). The main requirement for it grow, was that they be able to make sense and figure out the huge amount of data generated for its sellers so that they can position all their products in such a way that right kind of product is displayed to the right customer. What ebay.com needed was a product or service which could be able to handle thousands of petabytes data generated by customer’s activity on the site. And, this product or service should be

Since the 1970’s databases and report generators have been used to aid business decisions. In the 1990’s technology in this area improved. Now technology such as Hadoop has gone another step with the ability to store and process the data within the same system which sparked new buzz about “big data”. Big Data is roughly the collection of large amounts of data – sourced internally or externally - applied as a tool – stored, managed, and analyzed - for an organization to set or meet certain goals.

There is at least one field that is largely underutilizing big data and big analytics. This field is small business. Small businesses provide more than half of the jobs in the U.S. (Baldwin, 2015, para. 1), and yet, many of these businesses do not take advantage of the information that big data and big analytics can provide them. There are many ways big data and analytics can increase profits for small businesses, as long as these small businesses identify the opportunities that allow big data to work for them and as long as they work through the challenges that discourage small businesses from utilizing big data and analytics. This paper will discuss these opportunities and challenges in detail.

In order to overcome these limitations, a new database model known as Not Only SQL (NoSQL) database emerged with a set of new features. The main objective of NoSQL is not to discard SQL, but to be used as an alternative database data model for new features [1] [2] [3]. NoSQL database increases the performance of relational databases by a set of new characteristics and advantages. In contrast to relational databases, NoSQL databases introduced an additional feature that provides flexible and horizontal scalability and taking advantage of new clusters. The rise of NoSQL provides cost-effective management of data in modern web applications. With its new features, NoSQL can be used with applications that have a large transaction, and require low-latency access to huge datasets, service availability while

Savvas (2015) stated that the reasons why Marks and Spencers uses big data because it helps them to gain a better understanding of the customers of multichannels. It also helps them to understand more about the shopping habits of their customers, which in turn helps them to understand about inventory turnover. The higher the inventory turnover, the better their sales are. Marks and Spencer also uses big data because it helps them to answer key questions about IT support, even if they do not have any IT backgrounds, and at the same time, it helps empower their

The emergence of big data has provided different avenues for organizations to use data to improve different aspects of their respective operations. Be it customer service, research and development, or market position, Big Data has the potential to be a significant driving force in all these areas. However, there’s still a significant gap between the ability of Big Data to produce insightful analytical information based on real-time data and the ability of organizations to capture and utilize this readily available tool. This is, in part, due to the fact that the systems and processes necessary to fully maximize the usefulness of Big Data is currently lacking in most organizations. This lack of a conducive habitat for Big Data is further magnified in new organizations without any knowledge of Big Data. For organizations that have that have little to no knowledge of Big Data, there must be a thorough assessment of the benefits of big data and how they could improve the organizations overall place in the market. There also needs to be steps taken towards the design of frameworks that will enable the organization to better capture and utilize Big Data.

In a fast paced, business ordinated technological world the overall welfare of a company is tied to the success or failure to make the tough decisions. On one instance a company’s CEO might be able to make the choices based on experience, advice, or simple gut instinct. However, this is not the only skill one needs. There is a great deal of information to be found in being able to see investments in data and analytics. These decisions are based off of big data. Big data is a catch-phrase, used to describe a massive volume of both structured and unstructured data that is too large to process using traditional database and software techniques. The volume of data is in most cases is too big, moves too fast or it exceed the processing capacity the company has. Despite these potential drawbacks, big data contains the potential to help companies by improving operations and making faster, more intelligent decisions. This can be broken into three key parts, knowledge, data, and information.

In your business, you have your own big data challenges. You have to turn heaps of data about various entities into actionable information. The reporting needs of institutions have evolved from simple single subject queries to data discovery and enterprise-wide analysis that tells a complete story across the institution. While the volume, variety and velocity of big data seem overwhelming, big data technology solutions hold great promise. The way I see it we can use this as one of the biggest asset for the company. We have the capacity to see patterns recounting in real time across complex systems. Huron is marshalling its resources to bring smarter computing to big data. With the Huron big data platform, we are enabling our clients to manage data in ways that were never thought possible before.

Businesses using data is not a new concept; however, the role of data within industries has increased dramatically over the years to the point that it is essential for a business to understand how to handle data in order to continue operations. In today’s bustling digital age, professionals credit a certain type of data called “big data” with helping businesses gain insight on consumers. Big data is created whenever you travel to your favorite restaurant, make a particular move in a video game, swipe your card to purchase your favorite pair of Crocs, or tell your Facebook friends what you had for breakfast. It is data that is too large to be captured and processed by standard business

The industry is inundated with articles on big data. Big data news is no longer confined to the technical web pages. You can read about big data in the mainstream business publications such as Forbes and The Economist. Each week the media reports on breakthroughs, startups, funding and customer use cases. No matter your source for information on big data, one thing they all have in common is that the amount of information an organization will manage is only going to increase; this is what’s driving the ‘big data’ movement.

Relational databases play a major role in making many apps and programs work. They provide an easy way to store large amounts of data in a consistent, non duplicating, and maintainable way to be used by developers for analytical or software use ("Advantages of a relational database", n.d.). However, more and more applications and companies with a tremendous amount of data such as search engines, social networks, and e-commerce sites have been requiring a level of speed and scalability that relational databases can not provide ("Why NoSQL?", n.d.). NoSQL is a name given to a quickly growing type of database known as non-relational databases, which are being used to store and manage huge amounts of structured, semi-structured, and non-structured data known as "Big Data" ("Why NoSQL?" n.d.). With the advent of social networks and apps with millions of users, the rate of growth of non-structured and semi-structured data is exponential, and the value in being able to quickly traverse it, analyze it, and use it for development is also growing quickly (McGuire, Manyika, & Chui, 2012).

It has become hard to scale relational databases in the direction and to the degree needed to manage big data in a successful and less expensive way. Instead, a new system, known as “NoSQL” or “Not Only SQL”, has been created that makes the processing of terabytes and even petabytes of data possible (Paghy, “RDBMS to NoSQL”).

Today, data is a growing asset that various businesses are having difficulty converting into a powerful strategic tool. Companies need help turning this data into valuable insight, which can diminish risk and enhance returns on investments. Companies are struggling to make sense and obtain value from their big data. Superior and reliable

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.

The modern RDBMS advancements are not capable of supporting unstructured information with ideal space necessity. The plan winds up plainly mind-boggling and is henceforth troublesome for designers. The requirement for unstructured information administration is so annoying with conventional RDBMS arrangements (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). Moreover, RDBMS turns out to be an exorbitant answer for creating light-footed web applications with direct information investigation necessities. NoSQL is developing as a proficient possibility in this situation, which connects the issues related with RDBMS innovation. The market development can credit to creative dispatches of NoSQL arrangements, and collective endeavors by NoSQL sellers and clients. The endeavors of organizations, to enhance their market offerings, are creating the request of NoSQL, as a back-end bolster (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). The emergence of agile software development is creating the demand for NoSQL (Big data in financial services industry: Market trends, challenges, and prospects 2013 - 2018). They offer users much more avenues to accept data in many different forms. NoSQL is adaptable as SQL but offers many more uses that can apply to many organizations.