1.) Compare a traditional database with an analytical database and a NoSQL database.
2.) Compare THREE examples; each should be drawn from one of the following areas below:
a.) Databases (a traditional database, an analytical database, NoSQL database)
b.) Statistics Packages (such as SPSS, SAS, R, MiniTab, and MATLAB)
c.) API (including WEKA, Orange, Statistica, and Hadoop)
Describe your selected database, statistics package, and API or development environment and discuss how they are related and how each is used as part of an overall analytics system.
Sample solution
Dante Alighieri played a critical role in the literature world through his poem Divine Comedy that was written in the 14th century. The poem contains Inferno, Purgatorio, and Paradiso. The Inferno is a description of the nine circles of torment that are found on the earth. It depicts the realms of the people that have gone against the spiritual values and who, instead, have chosen bestial appetite, violence, or fraud and malice. The nine circles of hell are limbo, lust, gluttony, greed and wrath. Others are heresy, violence, fraud, and treachery. The purpose of this paper is to examine the Dante’s Inferno in the perspective of its portrayal of God’s image and the justification of hell.
In this epic poem, God is portrayed as a super being guilty of multiple weaknesses including being egotistic, unjust, and hypocritical. Dante, in this poem, depicts God as being more human than divine by challenging God’s omnipotence. Additionally, the manner in which Dante describes Hell is in full contradiction to the morals of God as written in the Bible. When god arranges Hell to flatter Himself, He commits egotism, a sin that is common among human beings (Cheney, 2016). The weakness is depicted in Limbo and on the Gate of Hell where, for instance, God sends those who do not worship Him to Hell. This implies that failure to worship Him is a sin.
God is also depicted as lacking justice in His actions thus removing the godly image. The injustice is portrayed by the manner in which the sodomites and opportunists are treated. The opportunists are subjected to banner chasing in their lives after death followed by being stung by insects and maggots. They are known to having done neither good nor bad during their lifetimes and, therefore, justice could have demanded that they be granted a neutral punishment having lived a neutral life. The sodomites are also punished unfairly by God when Brunetto Lattini is condemned to hell despite being a good leader (Babor, T. F., McGovern, T., & Robaina, K. (2017). While he commited sodomy, God chooses to ignore all the other good deeds that Brunetto did.
Finally, God is also portrayed as being hypocritical in His actions, a sin that further diminishes His godliness and makes Him more human. A case in point is when God condemns the sin of egotism and goes ahead to commit it repeatedly. Proverbs 29:23 states that “arrogance will bring your downfall, but if you are humble, you will be respected.” When Slattery condemns Dante’s human state as being weak, doubtful, and limited, he is proving God’s hypocrisy because He is also human (Verdicchio, 2015). The actions of God in Hell as portrayed by Dante are inconsistent with the Biblical literature. Both Dante and God are prone to making mistakes, something common among human beings thus making God more human.
To wrap it up, Dante portrays God is more human since He commits the same sins that humans commit: egotism, hypocrisy, and injustice. Hell is justified as being a destination for victims of the mistakes committed by God. The Hell is presented as being a totally different place as compared to what is written about it in the Bible. As a result, reading through the text gives an image of God who is prone to the very mistakes common to humans thus ripping Him off His lofty status of divine and, instead, making Him a mere human. Whether or not Dante did it intentionally is subject to debate but one thing is clear in the poem: the misconstrued notion of God is revealed to future generations.
References
Babor, T. F., McGovern, T., & Robaina, K. (2017). Dante’s inferno: Seven deadly sins in scientific publishing and how to avoid them. Addiction Science: A Guide for the Perplexed, 267.
Cheney, L. D. G. (2016). Illustrations for Dante’s Inferno: A Comparative Study of Sandro Botticelli, Giovanni Stradano, and Federico Zuccaro. Cultural and Religious Studies, 4(8), 487.
Verdicchio, M. (2015). Irony and Desire in Dante’s” Inferno” 27. Italica, 285-297.
Sample Answer
Sample Answer
Title: A Comparison of Databases, Statistics Packages, and APIs in Analytics Systems
Abstract
This report aims to compare traditional databases with analytical databases and NoSQL databases. Additionally, it compares three examples from different areas: databases (traditional, analytical, NoSQL), statistics packages (SPSS, SAS, R), and APIs (WEKA, Orange, Hadoop). The report describes each selected database, statistics package, and API or development environment, discusses their relationships, and examines how they are used as part of an overall analytics system.
Introduction
In the field of analytics, various tools and technologies play a crucial role in managing and analyzing data. Databases serve as repositories for storing and retrieving data, statistics packages offer advanced statistical analysis capabilities, and APIs or development environments provide frameworks for building analytics applications. This report aims to compare and contrast traditional databases with analytical databases and NoSQL databases. Furthermore, it highlights the differences between three examples from different areas: databases, statistics packages, and APIs in the context of an overall analytics system.
Comparison of Databases: Traditional, Analytical, and NoSQL
Traditional Database: A traditional database is a relational database management system (RDBMS) that stores data in tables with predefined schemas. It uses Structured Query Language (SQL) for data manipulation and retrieval. Traditional databases are designed for transactional processing and ensure data integrity through ACID (Atomicity, Consistency, Isolation, Durability) properties. They are suitable for applications with consistent and structured data requirements.
Analytical Database: An analytical database is specifically designed for complex queries and aggregations to support analytical processing. It optimizes read-heavy workloads by using columnar storage structures and indexing techniques. Analytical databases are optimized for high-speed query performance, enabling users to analyze large volumes of data efficiently. They are commonly used in business intelligence and data warehousing applications.
NoSQL Database: NoSQL databases provide flexible schema designs that allow for dynamic and unstructured data storage. They offer horizontal scalability and high availability by using distributed architectures. NoSQL databases are suitable for handling large amounts of unstructured or semi-structured data, such as social media data or sensor data. They are often used in big data applications and real-time analytics.
Comparison of Statistics Packages: SPSS, SAS, R
SPSS (Statistical Package for the Social Sciences): SPSS is a comprehensive statistics package widely used in social sciences research. It offers a graphical user interface (GUI) that simplifies statistical analysis for non-technical users. SPSS provides a wide range of statistical procedures, data visualization capabilities, and integration with other software tools. It is commonly used for survey analysis, data mining, and predictive modeling.
SAS (Statistical Analysis System): SAS is a powerful statistics package used for advanced analytics and business intelligence. It offers a wide range of statistical procedures, data manipulation capabilities, and sophisticated reporting features. SAS provides a programming language called SAS Language for complex data analysis tasks. It is commonly used in industries such as healthcare, finance, and marketing.
R: R is an open-source programming language and software environment for statistical computing and graphics. It provides a vast collection of packages for various statistical analyses, machine learning algorithms, and data visualization. R allows for extensive customization and flexibility in data analysis workflows. It is widely used in academia and research communities due to its robustness and extensive statistical capabilities.
Comparison of APIs/Development Environments: WEKA, Orange, Hadoop
WEKA (Waikato Environment for Knowledge Analysis): WEKA is a popular open-source software suite for machine learning and data mining tasks. It provides a graphical user interface (GUI) that allows users to build and evaluate machine learning models without extensive programming knowledge. WEKA supports various algorithms for classification, clustering, regression, feature selection, and more.
Orange: Orange is an open-source data analysis and visualization tool that offers a visual programming interface suitable for both novice and experienced users. It provides a range of statistical methods, machine learning algorithms, and data visualization techniques. Orange integrates well with Python scripting for advanced customization and analysis automation.
Hadoop: Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It provides a scalable and fault-tolerant architecture that allows for processing massive amounts of data in parallel. Hadoop includes the Hadoop Distributed File System (HDFS) for storage and MapReduce for distributed processing. It is commonly used in big data analytics to handle vast amounts of unstructured or semi-structured data.
Relationships and Usage in an Analytics System
In an overall analytics system, these components work together to enable effective data management and analysis:
Databases act as the foundation by storing structured or unstructured data collected from various sources.
Statistics packages offer advanced statistical analysis capabilities to derive insights from the stored data.
APIs or development environments provide frameworks for building analytics applications or integrating existing tools into customized workflows.
For example:
A traditional database can store transactional data collected from an e-commerce website.
An analytical database can be used to aggregate and analyze customer behavior patterns from the transactional data.
A NoSQL database can store unstructured customer feedback from social media platforms.
SPSS can perform statistical analysis on customer survey data collected from the database.
R can be used to build predictive models to forecast customer preferences based on historical sales data.
Hadoop can be utilized for distributed processing of big data sets stored in the databases.
These components work synergistically to enable organizations to make informed decisions based on extensive data analysis.
Conclusion
Databases play a crucial role in storing and managing data in analytics systems. Traditional databases are suited for structured transactional data, while analytical databases optimize query performance for complex analytical tasks. NoSQL databases handle unstructured or semi-structured big data sets effectively. Statistics packages like SPSS, SAS, and R offer advanced statistical analysis capabilities to extract insights from the stored data. APIs or development environments like WEKA, Orange, and Hadoop provide frameworks for building analytics applications or integrating existing tools into customized workflows. Understanding the strengths and characteristics of these components helps organizations design effective analytics systems that align with their specific requirements.
References
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Website Name. (Year). Title of webpage. Retrieved from [URL]
