Unprecedented amounts of data being stored by businesses are driving the need for new database technologies that will scale and perform faster than the traditional relational database model. The No-SQL movement has brought these new technologies to the forefront and several new methods of storing data have been introduced. With the storage of data also comes the need to access the data by applications that are used by enterprises to manage their business and also the applications that customers use to interact with the enterprise. This will provide many challenges to software developers who are well versed in accessing data stored within the traditional relational database model through the use of Simple Query Language (SQL) and other technologies such as XML, XQuery and JSON. Whilst the No-SQL movement brings many different ways to store data in many different formats -each supporting different business models and the type of data that is being stored - a generic language that provides the functionality that SQL delivered to developers has not yet been realised. Recently there have been attempts within the open source community to address this issue with the most significant of these projects being UnQL and JSONiq both of these new query languages aspire to be the new generic language for NoSQL databases.
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NoSQL
Defining NoSQL can be quite a challenge and the name is problematic in that doesn’t describe the NoSQL movement very well. Carlo Strozzi first coined the name NoSQL in 1998 when he developed a relational database that had no form of the SQL Language for querying the data. (Williams, 2012) In 2009 an employee from a company named ‘Rackspace’ used the term NoSQL to cover a collection of open sourced non-relational distr...
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The next project deliverable is a robust, modernized database and data warehouse design. The company collects large amounts of website data and uses this data to analyze it for the company’s customers. This document will provide an overview of the new data warehouse along with the type of database design that has been selected for the data warehouse. Included in the appendix of this document is a graphical depiction of the logical design of the
The common element of the open source databases is the use of share data of student information. The common features used by the databases are their operating systems UNIX or Windows, as well as MYSQL database. The database used b...
What technology can do for a company is important. A database takes information from one location and sends it to another location. Amazon’s database is the backbone to the company because Amazon is an e-commerce base company. Amazon runs off of a Linux- based database. As of 2...
Oracle's relational databases represent a new and exciting database technology and philosophy on campus. As the Oracle development projects continue to impact on University applications, more and more users will realize the power and capabilities of relational database technology.
In 1977, Larry Ellison, Bob Miner, and Ed Oates founded System Development Laboratories. After being inspired by a research paper written in 1970 by an IBM researcher titled “A Relational Model of Data for Large Shared Data Banks” they decided to build a new type of database called a relational database system. The original project on the relational database system was for the government (Central Intelligence Agency) and was dubbed ‘Oracle.’ They thought this would be appropriate because the meaning of Oracle is source of wisdom.
1. Introduction In the year 2016, the size of the market for big data analytics in India was two billion USD and it is expected to grow approximately by eight times to reach around sixteen billion USD by the year 2025, as per the National Association of Software and Services Companies (NASSCOM)1. India currently ranks among the top ten big data analytics market of the world1. According to Mr. K. S. Viswanathan, Vice President, NASSCOM, the big data sector in India is expected to grow at a Compounded Annual Growth Rate (CAGR) of twenty six percent over the next five years and it will have a thirty two percent share in the global market2.
Dr. Edgar F. Codd was best known for creating the “relational” model for representing data that led to today’s database industry ("Edgar F. Codd") (Edgar F. Codd). He received many awards for his contributions and he is one of the many reasons that we have some of the technologies today. As we dig deeper into his life in this research paper, we will find that Dr. Edgar F. Codd was in fact, a self-motivated genius.
The cloud storage services are important as it provides a lot of benefits to the healthcare industry. The healthcare data is often doubling each and every year and consequently this means that the industry has to invest in hardware equipment tweak databases as well as servers that are required to store large amounts of data (Blobel, 19). It is imperative to understand that with a properly implemented a cloud storage system, and hospitals can be able to establish a network that can process tasks quickly with...
Kasdorf, B. (2014). Welcome to the metadata millenium. Book Business, 17(1), 18-23. Retrieved from http://search.proquest.com/docview/1500945974?accountid=10043
The key to Amazon’s strategy is the IT infrastructure’s ability to deal with more than a million requests at a consistent, error-free rate (Demir, 2017, p.12). Not to mention, Amazon’s Web Services makes up for about 10 percent of their total revenue. The first big play for Amazon’s Web Services was the launch of DynamoDB which sent customer data to multiple databases creating a strong collaboration system. By testing this system for long periods of time, Amazon analyzed the faults within it. However, with this jewel, engineers expanded new features and algorithms within the system. In order to get the design up to expectations, engineers improved the mastery of independent codes. Throughout the complexity of Amazon’s expansions, AWS has played a pivotal role in the Systems Developmental Life Cycle. “As an example of this growth, in 2006, AWS launched S3, its Simple Storage Service…Less than a year later it had grown to two trillion objects and was regularly handling 1.1 million requests per second” (Newcombe, 2015, p. 66). Developing such systems has given Amazon the ability to establish unforeseeable innovation which continues to astonish the entire world. As everything has become newer and improved through technology, Amazon implements this into each and every
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
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Data center, in the context of big data, is not only for data storage but it plays significant role to acquire, manage and organize the big data. Big data has uncompromising requirement for storage and processing capacity. Hence the data center development should be the focus for effective and rapid processing capacity. With the increasing scale of data centers, the operational cost should be reduced for the development of data centers. Today’s data centers are application-centric, powering the many business applications, standalone websites and e-commerce offerings on the web. Tomorrow’s data centers need to be data centric: storage and infrastructure capacity must be expanded to support IoT/Big Data-generated information. This also affects future bandwidth in data centers as resources will be mostly consumed by IoT sensors and machines, as opposed to user activity and behaviour.
Although the term Big Data has been well known by people, there is no common definition of it. Among various existing definitions of big data, the one put forward by Gantz and Reinsel in 2011 is relatively more recognized. Their definition claims that big data represents very large volumes of all kinds of data [2]. Another widely recognized definition is proposed in a report of McKinsey & Company [3]. The report claims, “Big data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze”. From these definitions, we can know that one important feature of big data is large size. However, the definition of “large size” may vary over time. In recent years, with the emergence and development of social networks (e.g. Facebook, twitter), electronic commerce (e.g. eBay, Amazon) and other network platforms, volume of data grew very fast. We used to regard gigabyte (GB) as “large size”, but nowadays only above petabyte (PB, equals to 106 GB) level can be called “large size”. Although large volume is one characteristic of big data, it doesn’t mean that big data is equivalent to massive data. There are also some other characteristics that can distinguish
petabytes of junk. Data must be accessible and query-able. When we say that we have