Management issues associated with traditional data management and with big data management.

by | Jan 27, 2025 | Marketing

 

Compare the management issues associated with traditional data management and with big data management. Include data warehousing and Hadoop in your discussion. Also discuss the applications for these systems and future trends.

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.


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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.

Management Issues in Traditional vs. Big Data Management

Traditional Data Management

  • Focus: Primarily structured data (relational databases) with well-defined schemas.
  • Volume: Relatively smaller datasets.
  • Velocity: Data is typically collected and processed in batches.
  • Variety: Limited to structured data (numbers, text, dates).
  • Veracity: Data quality is generally higher due to controlled collection and storage.
  • Management Issues:
    • Scalability: Limited scalability to handle rapidly growing data volumes.
    • Data Silos: Data often resides in isolated systems, hindering cross-functional analysis.
    • Limited Flexibility: Difficulty in handling unstructured and semi-structured data.
    • Slow Data Processing: Batch processing can lead to delays in insights.

Big Data Management

  • Focus: Handles a wide variety of data types: structured, semi-structured (e.g., JSON, XML), and unstructured (text, images, videos).
  • Volume: Deals with massive datasets that cannot be easily processed by traditional tools.
  • Velocity: Handles data in real-time or near real-time, enabling rapid insights.
  • Variety: Accommodates diverse data sources, including social media, sensor data, and IoT devices.
  • Veracity: Data quality can be a significant challenge due to the diversity and volume of data.
  • Management Issues:
    • Data Integration: Integrating data from diverse sources can be complex and challenging.
    • Data Quality: Ensuring data accuracy, completeness, and consistency is crucial but difficult to achieve at scale.
    • Data Security and Privacy: Protecting sensitive data from unauthorized access and breaches is paramount.
    • Skill Gap: Finding and retaining skilled professionals with expertise in big data technologies is challenging.
    • Cost: Investing in the necessary infrastructure and tools for big data management can be expensive.

Management Issues in Traditional vs. Big Data Management

Traditional Data Management

  • Focus: Primarily structured data (relational databases) with well-defined schemas.
  • Volume: Relatively smaller datasets.
  • Velocity: Data is typically collected and processed in batches.
  • Variety: Limited to structured data (numbers, text, dates).
  • Veracity: Data quality is generally higher due to controlled collection and storage.
  • Management Issues:
    • Scalability: Limited scalability to handle rapidly growing data volumes.
    • Data Silos: Data often resides in isolated systems, hindering cross-functional analysis.
    • Limited Flexibility: Difficulty in handling unstructured and semi-structured data.
    • Slow Data Processing: Batch processing can lead to delays in insights.

Big Data Management

  • Focus: Handles a wide variety of data types: structured, semi-structured (e.g., JSON, XML), and unstructured (text, images, videos).
  • Volume: Deals with massive datasets that cannot be easily processed by traditional tools.
  • Velocity: Handles data in real-time or near real-time, enabling rapid insights.
  • Variety: Accommodates diverse data sources, including social media, sensor data, and IoT devices.
  • Veracity: Data quality can be a significant challenge due to the diversity and volume of data.
  • Management Issues:
    • Data Integration: Integrating data from diverse sources can be complex and challenging.
    • Data Quality: Ensuring data accuracy, completeness, and consistency is crucial but difficult to achieve at scale.
    • Data Security and Privacy: Protecting sensitive data from unauthorized access and breaches is paramount.
    • Skill Gap: Finding and retaining skilled professionals with expertise in big data technologies is challenging.
    • Cost: Investing in the necessary infrastructure and tools for big data management can be expensive.

Data Warehousing vs. Hadoop

  • Data Warehousing:
    • Focus: Structured data for business intelligence and reporting.
    • Strengths: Well-established technology with mature tools and techniques.
    • Limitations: May struggle with the volume, velocity, and variety of big data.
  • Hadoop:
    • Focus: Handling massive volumes of unstructured and semi-structured data.
    • Strengths: Highly scalable and fault-tolerant, suitable for batch and real-time processing.
    • Limitations: Complex to set up and manage, requires specialized skills.

Applications

  • Traditional Data Management:

    • Customer Relationship Management (CRM) systems
    • Financial transaction processing
    • Inventory management
    • Business intelligence reporting

  • Big Data Management:

    • Social media analytics
    • Fraud detection
    • Internet of Things (IoT) applications
    • Scientific research
    • Personalized medicine
    • Predictive maintenance

Future Trends:

  • Edge Computing: Processing data closer to the source to reduce latency and bandwidth requirements.
  • Artificial Intelligence (AI) and Machine Learning (ML): Integrating AI/ML algorithms for advanced data analysis, pattern recognition, and predictive modeling.
  • Data Governance and Compliance: Implementing robust data governance frameworks to ensure data quality, security, and compliance with regulations.
  • Cloud Computing: Leveraging cloud platforms for scalable and cost-effective big data management and analytics.

Conclusion

While traditional data management systems have served businesses well, the rise of big data necessitates new approaches. By addressing the challenges associated with big data management and leveraging emerging technologies, organizations can unlock valuable insights from their data and gain a competitive advantage in the digital age

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