Database in Distributive Environment
Database is a diverse collection of information which manages data and allows fast storage and retrieval of that data. Each application requires database to hold the data specific to the application, which is accessed by the users. However, each application according to its requirement needs different type of database. Researchers classify the databases according to the user specific functionalities, parameters as well as application.
There have been several discussions and researches on the joins, which is a key performance indicator of any database. Some researchers outweighed the centralized database over distributed database, based on the analysis of joins performed in above mentioned two databases. For example, Sharma and Singh (2012) conclude that in centralized database data is placed at central location while in distributed database, data is distributed among several locations to increase access transparency. They found that data is placed over a central location to avoid any redundancy in the database. In contrast, Carbunar and Sion (2012) explain that sensitive data in parallel distributed system are placed by a client on a database server situated at service provider. On the basis of joins performed the authors found that the server should not be able to evaluate inter-column join predicates on initially stored data.
In addition, since the join performance determines the speed of databases, some authors found that cloud database is better than parallel database. Cheng, Yu and Yu (2011) show that the HPSJ algorithm processes an R-join between two base relations, it first gets all centers that have a nonempty x-labeled F sub cluster and a nonempty y-labeled T sub cluster, using the table and maintains them. The authors describe that two step R-join algorithm is used to process temporal relation that contains R-join attributes. On the other hand, Carbunar and Sion (2012) explain that join algorithms returns all matching tuple which makes parallel database faster. Different authors see the parameters according to the use in specific application.
According to researchers another category is load balancing. For example Lubbe, Reuter and Mitschang (2012) proposed an algorithm for load balancing of partitioned data. This aims at balancing the amount of data and focus on reducing data skew between partitions. They also showed that if current load rises above some certain threshold in a particular node then it will check the load in the neighboring node and if the load in that node is below the threshold then the load will be shared amongst them.
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
Now click the “ENTER” key on your keyboard, on your computer is operating in ‘Safe Mode’.
Now we can say that an enterprise data warehouse could be used to manage the big data and the extreme workloads but we would find that often it is more efficient to preprocess the data before storing it in the warehouse. Let’s consider an example even data from hardware sensors have a lar...
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.
Considering the fact that most of the companies computers are personal computers it is only proper then that a relation database is implemented to ensure that the most sensitive and critical data is managed. A database management system will be used in capturing and analyzing data, the system is designed in such a manner that it is able to interact with information user. The captured data will be fitted into categories that are predefined. The tables containing the data will be composed of columns and rows, the columns will contain data category while as the row will contain a unique description about the data in the column (Alagić,
It presents a novel approach by providing a parallel two-way integration with Hadoop. All writes from the real-time tier make it into Hadoop and output of analytics inside Hadoop can emerge in the in-memory “operational” tier and distributed across data centres. The idea is to leverage distributed memory across a large farm of commodity servers to offer a very low latency SQL queries and transactional updates. 6) Strategic Direction Pivotal’s road map has given a strategic direction to its Hadoop solution and has made it significantly more competitive; its innovations focuses on improving the HAWQ SQL engine and integration with other Pivotal
System performance is one of the most important metrics to measure system efficiency. Organizations that need high-performance computing such as scientific ones can use cloud computing to perform their tasks faster. In the cloud there are many machines works together in parallel to provide high processing speed. Now, most of organizations use only 15 percent of their machines capacity. Moreover, cloud computing provides services for data-intensive applications such as data mining. Salesforce.com which provides cloud CRM applications says that the performance is faster five times comparing to traditional cline-server infrastructure.
[7] Elmasri & Navathe. Fundamentals of database systems, 4th edition. Addison-Wesley, Redwood City, CA. 2004.
An OLAP application is targeted to deliver most responses to users within about five seconds, with the simplest analyses taking no more than one second and very few taking more than 20 seconds. Impatient users often assume that a process has failed if results are not received with 30 seconds, and they are apt to implement the ‘3 finger salute’ or ‘Alt+Ctrl+Delete’ unless the system warns them that the report will take longer. Even if they have been warned that it will take significantly longer, users are likely to get distracted and lose their chain of thought, so the quality of analysis suffers. This speed is not easy to achieve with large amounts of data, particularly if on-the-fly and ad hoc calculations are required. A wide variety of techniques are used to achieve this goal, including specialized forms of data storage, extensive pre-calculations and specific hardware requirements, but a lot of products are yet fully optimized, so we expect this to be an area of developing technology. In particular, the SAP Business Warehouse is a full pre-calculation approach that fails as the databases simply get too. Likewise, doing everything on-the-fly is much too slow with large databases, even if the most expensive server is used. Slow query response is consistently the most often-cited technical problem with OLAP products.
Inconsistently storing organization data creates a lot of issues, a poor database design can cause security, integrity and normalization related issues. Majority of these issues are due to redundancy and weak data integrity and irregular storage, it is an ongoing challenge for every organization and it is important for organization and DBA to build logical, conceptual and efficient design for database. In today’s complex database systems Normalization, Data Integrity and security plays a key role. Normalization as design approach helps to minimize data redundancy and optimizes data structure by systematically and properly placing data in to appropriate groupings, a successful normalize designed follows “First Normalization Flow”, “Second Normalization Flow” and “Third Normalization flow”. Data integrity helps to increase accuracy and consistency of data over its entire life cycle, it also help keep track of database objects and ensure that each object is created, formatted and maintained properly. It is critical aspect of database design which involves “Database Structure Integrity” and “Semantic data Integrity”. Database Security is another high priority and critical issue for every organization, data breaches continue to dominate business and IT, building a secure system is as much important like Normalization and Data Integrity. Secure system helps to protect data from unauthorized users, data masking and data encryption are preferred technology used by DBA to protect data.
A database-management system (DBMS) is a group of organized data and a set of programs have access to some data. This is a collection of related data without any doubt meaning and thus is a database. The collection data usually referred to as the database, have something information directly connected to an enterprise. The main goal of a DBMS is to provide a way to store up and save some of database information that is both well-located and useful. From data, the user known facts that can be recorded and that have trusted the meaning. For example, consider the names, telephone numbers, and addresses of the people may we know.
Databases are becoming as common in the workplace as the stapler. Businesses use databases to keep track of payroll, vacations, inventory, and a multitude of other taske of which are to vast to mention here. Basically businesses use databases anytime a large amount of data must be stored in such a manor that it can easily be searched, categorized and recalled in different means that can be easily read and understood by the end user. Databases are used extensively where I work. In fact, since Hyperion Solutions is a database and financial intelligence software developing company we produce one. To keep the material within scope I shall narrow the use of databases down to what we use just in the Orlando office of Hyperion Solutions alone.
System performance is one of the most critical issues faced by companies dealing with vast amounts of data. Companies use database systems and their applications to store, retrieve and handle this data.
Relational database management systems and desktop statistics and visualization packages often can’t handle Big Data. The work can require anything from tens to thousands of servers. Big data varies depending on the capabilities of the users and their tools.
The Database Management System (DBMS) is software that enables the users to define, create, maintain and control the access to the database. It is a software that interact with the user’s applications programs and it database. Meanwhile, information retrieval system is a system that involved the activity that the systems obtain the information. The obtaining information action need the information from it resources.