What Is The Tools And Architecture For Big Data Chapter Summary

Cloud computing was a completely new term a short 9 years ago, in 2007. The basis of this technology is to move the workload of IT activities away from an organization, and to one or more third parties that have resources dedicated to processing such things. These can be, but are not limited to, networking, storage, software systems, and applications. Rather than having to create and maintain their own expensive datacenters, companies can pay a fee to use someone else’s. This makes growing businesses extremely flexible, as they can easily gain or remove storage space per their needs. Being able to purchase the use of online storage space is known as “hardware as a service,” or, more simply, “virtualization.” Being able to purchase the use of online software is known as “software as a service.” Both are very powerful tools that allow the minimization of a company’s IT budget.

Brown, B., Chiu, M., Manyika, J. (2011), Are you ready for the era of big data? Retrieved

This is one of the greatest challenge that is acting as a key barrier in widespread adoption and pervasive use of the tools fostering for development and progress in this field of study. The real time of the big data analytics would require some of the unique features and special computing powers and potentials (Gantz, 2012). Tools have to made specially advanced so as to incorporate process terms in real time (Chen, 2012). Every business oriented organization should be transformed into information centric (Kaisler, 2013) to focus upon the real time data analysis in terms of both input and output.

Big Data is an expansive phrase for data sets so called big, large or complex that they are very difficult to process using traditional data processing applications. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. In common usage, the term big data has largely come to refer simply to the use of predictive analytics. Big data is a set of techniques and technologies that need or require new forms of integration to expose large invisible values from large datasets that are diverse, complex, and of a massive scale. When big data is effectively and efficiently captured, processed, and analyzed, companies

With big data seeming to boom so fast, it 's not surprising that problems in the processing of these enormous data sets were overlooked. With something so popular still in the experimental phase, there is a multitude of troubles that arise from the lack of rules or guides to limit how researchers manipulate the data in order to pull out the correlations that many big data scientists discover.

In 2010, a massive technological movement forged itself into the financial sector. It was a new and exquisite type of computing system, and its introduction changed the manner in which the finance industry operated. This change engendering movement is cloud computing. As technology has continued on its path of rapid advancement, customer demand and expectation has done the same. In the financial sector, the consumer base has grown exponentially, thereby forcing financial firms to deal with enormous amounts of data on a daily basis. To keep track of this data and information is a titanic challenge, however, the emergence of cloud computing has proven to be an excellent

Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems:Putting analytics and big data in cloud. Decision Support Systems, 412-421.

Five years ago, few people had heard the phrase ‘Big Data.’ Today, it’s hard to go an hour without seeing it implemented practically in our daily life. The promise of a highly accurate data-driven decision-making tool is an attractive lure for any organization in any industry. However, big data is not without its own problems.

Big Data has taken the business world by storm. By 2020, it is expected that the amount of digital information in existence will have grown from 3.2 zettabytes in 2014 to 40 zettabytes. Companies are doing all they can to capture this digital information and turn it into actionable insights. Currently, the total amount of data being captured and stored by industry is doubling every 1.2 years. Therefore, companies must find increasingly efficient solutions to store and analyze this incredible amount of data.

Recently, Cloud technology has turn out to be a huge buzzword, and with good reason. The cloud already creates remarkable value for clients and businesses by making the digital world simpler, faster, more powerful, and more efficient. In addition to bringing valued Internet-based services and applications, the cloud can provide a more

The emergence of new technologies, applications and network systems makes it hard to run the current business models and huge data types, and thus emerged various types of analytic tools like Big Data, which make this work easier by way of proper organization of data. Big Data is all about analyzing different forms of data (Structured, Semi-structured and Un-structured) and it is not about the procedure, creation or consumption of data.

Big Data and Analytics systems are fast emerging as one of the most critical system in an organization’s IT environment. But with such a huge amount of data, there come many performance challenges. If Big Data systems cannot be used to make or forecast critical business decisions, or provide insights into business values hidden under huge amounts of data at the right time, then these systems lose their relevance. This article talks about some of the critical performance considerations in a technology agnostic way. These should be read as generic guidelines, which can be used by any Big Data professional to ensure that the final system meets all performance requirements.

In recent years, there has been an increasing emphasis on big data, business analytics, and “smart” living and work environments. Though these conversations are predominantly practice driven, organizations are exploring how large-volume data can usefully be deployed to create and capture value for individuals, businesses, communities, and governments (McKinsey Global Institute, 2011). Big data refers to data volumes in the range of exabytes (1018) and beyond. Such volumes exceed the capacity of current on-line storage systems and processing systems. Data, information, and knowledge are being created and collected at a rate that is rapidly approaching the exabyte/year range. But, its creation and aggregation are accelerating and will

As an obvious fact, we have lots of data in various fields. Actually, it is estimated that the amount of useful data produced will be over 15 zettabytes by 2020, compared with 0.9 zettabytes in 2013. [IDC 's Study 1] This has led to an unavoidable challenge, however, data users have to figure out a way to properly store and effectively analyze the large-scale data.\

A Data WareHouse is a type of database normally used by large companies to store large amounts of data in and have the data be easily accessible. They are normally set up in one of three set-ups. The basic model that takes data straight from it sources, such as operational systems and flat files. The Staging Mode that has a staging area that takes the data, from the systems and files before moving it to data warehouse. The Final type adds data marts, a small database that takes specific information from the data warehouse, to the previous model between the data warehouse and the end users. Data Warehouses are also really useful because they make it easy to pull data from either queries or data mining. Data warehouses are a useful tool when dealing with large amounts of data.