Architecture Of Big Data On E Marketing Essay

Data objects can model relational data or advanced data types such as graphics, movies, and audio. Smalltalk, C++, Java, and others are objects used in object-oriented data. The object-relational is a combination of relational and object-oriented databases. Traditional and advanced data types can be used to construct database management systems. These systems can connect to a company’s website and update records as needed. Database Approach The main purpose of a database is data storage that can be stored and retrieved when needed. A popular common language called structured query language (SQL) is used to store and retrieve data in relational database. This language enables the systems to run a report or modify data or remove the data from the database. A database management system (DBMS) controls all aspects of a database, this is not limited to the creation, maintenance, and use of database. The DBMS ensures proper applications are able to access the database. An important purpose of a DBMS is to maintain the data definitions (data dictionary) for all the data elements in the database. It also enforces data integrity and security measures. Data Models Data models provide a contextual framework and graphical representation that aid in the definition of data elements. In a relational database, the data model lays the foundation for the database and identifies important entities,

It is a process of constructing a data model for each view of the real world problem which is independent of physical considerations. This stage involves assessing the informational needs of an organization so that a database can be designed to meet those needs.

Data is a collection of distinct pieces of information, particularly information that has been in some specific way for use in analysis or making decisions.

Data is defined as useful raw material which is intended to be useful for both the originator and for the intended receiver. Data consists largely of facts and figures ideal for communicating the intended meaning. This data can be interpreted and can be categorised as follows;

The data structure represents the logical relationships between data elements. In addition the data also determines the organizational structure, access methods, and alternative processing assosiativitas level for information.

Data comprises of factual information. Data are the facts from which information is derived. Data is not necessarily informative on its own but needs to be structured, interpreted, analysed and contextualised. Once data undergoes this process, it transforms in to information. Information should be accessible and understood by the reader without needing to be interpreted or manipulated in any way.

24) Before it can be loaded into the data warehouse, operational data must be extracted and

In all information systems, data resources must be organized and structured in some logical manner, so that they can be:

Primary Data: This is data that you collect/measure by yourself. For example; you may decide to look at the effects of exercise on the Heart Rate. In primary data you design the experiment and carry out yourself on a group of subjects.

There might be multiple source systems or legacy systems that need to be considered as part of the data conversion and migration and it is expected to understand a reasonable portion of the intended use of each of these underlying source systems. One of the other activities in the agenda is to give a deep dive in to the different stages of migration and the connectivity between the stages of Extract, Transform and Load. Introduction to various environments that host or stage the data is also part of the plan.It is also anticipated to have introduction to the data flow and conversion from source to

(b) A conceptual schema of all the data generated in the enterprise and how it was all related

In this paper the writer will seek to respond to the questions designated for both scenarios. This paper will list typical fields for each type of data. Provide an example of two relationships that you need to track. This paper will also answer the questions of: Do you need a database system? If not, can Excel® handle the data and the output? What are the advantages and disadvantages? Would you use a personal database or an enterprise database? Explain your answer. Would a decision support system (DSS) be helpful? Explain your answer.

Before we talk about Fundamentals of database management system, we need to know what is a ‘Data’. Data is a something which has an implicit meaning and that can be deduced to new facts which can be recorded too.

Due to the rapid growth in the use of Internet and its connected tools, an enormous amount of data are being produced on a daily basis. The concept of big data arrives when we were unable to manage this huge data with traditional methods. Big data is a mechanism of capturing, storing and analyzing the big datasets and also an idea of extracting some value from it. It is very handful while determining the root causes of failures, issues and defects in near-real time, creating coupons and other sales offers according to the customers shopping patterns, detecting any suspicious and fraudulent activities in real-time. As it is very advantageous, it also has some issues. Some of the common issues can be characterized into heterogeneity, complexity, timeless, scalability and privacy. The most important and significant challenge in the big data is to preserve privacy information of the customers, employees, and the organizations. It is very sensitive and includes conceptual, technical as well as legal significance.

Already some success is achieved from big data in some fields like Sloan digital sky survey so it’s mean there is some potential in big data and benefits are also real but still some challenges like scalability, heterogeneity, integration, privacy, security etc. need to be addressed for realizing full potential of big data. One of the major challenge is transformation of unstructured data to structured form for accurate and timely processing. Challenges with big data starts with very first phase of big data analysis pipeline that is data acquisition phase. It’s a challenging task to determine what data to keep, what to discard and how to efficiently store the data. Other challenges are faced in data cleaning, integration and data analysis phase of big data analysis pipeline.