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Comparison of Big Data Tools: Tableau and TIBCO

   

Added on  2019-09-22

7 Pages2438 Words62 ViewsType: 62
Data Science and Big DataStatistics and ProbabilityDatabases
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Magic Quadrant for Analytics and Business Intelligence Platforms
Comparison of Big Data Tools: Tableau and TIBCO_1

Executive SummaryIn recent time data and information is knowledge and knowledge is power and invariably power converts into money. Now the more the data you have the more money you make. It is proved that data & information both work as a support system for both consumer and business. TIBCO and Tableau is a powerful and well rounded platform with deployment and development capabilities. The main problem arises when privacy is invaded, as well as sensitive information and data is lost and stored. Due to this individual or company security and privacy ethics become very vulnerable. Many of the business houses use so many advanced algorithmic analysis to extract data and information so that they can use it to spot the trends of the market and also prevent the different ethical issues. The way nowadays people use the internet, it is a very high probability that someone out there knows more about the people than people know about himself. Misuse of information is also coming under major risk. The information which is obtained ethically can be used for different unethical purposes. If any one want to bring togather all the facilities in one umbrella then it is TIBCO and Tableau. Here in this report ,all things are going to explain about magic quadrant for analytics and Business Intelligence using SWOT analysis.
Comparison of Big Data Tools: Tableau and TIBCO_2

IntroductionIn recent times due to rise in use of new internet and computer technology in every sector of life, increases the problems related to the security and privacy of data and information during their transmission period. Today in modern times so many people are working on distribution, collection,and execution of data as well as information in every business they do. Due to this at the same time,we are facing so many problems in this information age. As we know that data and information is responsible for the formation of intellectual property by which people craft their lives and secure their privacy and dignity. Personal data and information related to legal problems are collected by court and other legal authority. Change from paper to electronic or digital record needs a change in the security methods and various security measures to prevent this type of threat. The social problem caused due to this can be solved with full dignity in this information age. In this there are so many different types of ethical issues are present. According to Gartner ,When challenger move to leader then gartner state that TIBCO and Tableau are powerful and well rounded platform. Due to its end to end and deployment abilities , TIBCO and Tableau manage the data center of IOT analytics. Data sources and ManagementNowadays the amount of data in the world has been increasing at an exponential rate andexperiencing the growth of 50% per year. Due to this Data-set become so large and complex whichleads to the emergence of new database management tools such as 1)Open Innovations 2)Open Data3)Open Source(e.g Hadoop). There are also three main characteristics of big data i)volume(dataquantity)(ii)Velocity(Data Speed) (iii)Variety(Datatypes)A big player from the web like Facebook, Google and Amazon handled their data based uponcustomer interactions with their services. They develop many big data tools to collect, store andanalyze large quantities of data like Dynamo DB, Big Table, Cassandra, and Hadoop. Facebook is apopular social network with 1.2 billion users worldwide. Apart from google, facebook is the onlycompany which stores a high level of detailed customer information. To increase the result of datacollections agencies and researchers has access to many more variables which are strictly needed toanswer their original hypothesis. So many times the collected data are not fully explored or used bythe original research team due to the limitation in time, interest or resources. If different researchteam want to share their data with other researchers who have the resources, skills, and interest tocarry out additional analysis. It can hugely increase the productivity of the research team, whichconducted the original study. This type of information exchanges mainly involves an agreementbetween the data analysis team and data collection team to clear out the details about the datasharing protocols and how the data should be used.SWOT analysis of Tableau and TIBCO platform
Comparison of Big Data Tools: Tableau and TIBCO_3

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