Explain The Difference Between Archiving And Database Backup

Explain The Difference Between Archiving And Database Backup Why A

Explain the difference between archiving and database backup. Why are they both important? (35%)

Assume you are working for a financial institution. What information do you feel needs to be archived? Give specific examples of government laws and regulations that may apply. (30%)

The DBA denormalized some of the data in the Premier Products database, and one of the resulting tables is the following: Orders (OrderNum, OrderDate, CustomerNum, CustomerName, Street, City, State, Zip, PartNum, Description, NumOrdered, QuotedPrice). Which field or fields cause the table to no longer be in third normal form? In which normal form is the table (copy the appropriate normal form definition and explain why the table conforms to the that rule, use field names from this exercise to demonstrate your understanding of the content). (20%)

Writing Org/Clarity/Spelling/APA (15%)

Assignment word count must be 700+.

No more than 20% of the narrative portion of the submission may be direct quotes, original content is required. Include references for each source. Submit the response in a word-compatible document.

Paper For Above instruction

The distinction between archiving and database backup is fundamental to understanding data lifecycle management and ensuring organizational data integrity and compliance. Although both processes serve to protect data, they fulfill different roles within the broader scope of data preservation. Analyzing these differences reveals why organizations must implement both strategies effectively, especially in sectors like finance where data accuracy and regulatory compliance are paramount.

Database backup involves creating a copy of current database data, primarily to safeguard against data loss due to hardware failure, corruption, or accidental deletion. Backups are typically made at regular intervals—daily, weekly, or as dictated by organizational policies—and are stored in a manner that facilitates rapid restoration in emergencies. They serve as snapshots of data at specific points in time, allowing restoration to minimize downtime and minimize the impact of data loss.

Archiving, on the other hand, refers to the systematic process of moving older, infrequently accessed data from active databases to separate storage. Unlike backups, archives are meant for long-term retention and compliance with legal or organizational policies. Archived data is preserved in a way that maintains its integrity and facilitates retrieval for historical reference, audits, or regulatory reporting. It is not typically used for restoring current operations but rather for retaining valuable historical information over extended periods.

The importance of both processes stems from their roles in organizational data governance. Regular backups ensure resilience against technical failures and enable operational continuity. Meanwhile, archiving helps organizations comply with legal mandates, reduce operational overhead, and preserve data that may be required for audits or historical analysis. For instance, financial institutions are often mandated to retain transaction records for several years due to government regulations, making archiving crucial for legal compliance.

In the context of a financial institution, critical information that needs to be archived includes transaction records, account statements, and client communication histories. Specific examples includes Securities and Exchange Commission (SEC) regulations such as the Securities Exchange Act of 1934, which mandates record-keeping for securities transactions. The Dodd-Frank Act also requires detailed documentation of financial transactions and compliance reports. Additionally, regulations like the Gramm-Leach-Bliley Act stipulate the retention of customer information to ensure privacy and security over extended periods. Failure to comply with these legal requirements can result in substantial penalties, lawsuits, and loss of credibility.

In practical terms, archiving in a financial institution involves storing data like trade confirmations, account opening documents, compliance reports, and audit trails. The data must be retained securely, often in encrypted form, and accessible for regulatory review over periods that can extend to several decades depending on jurisdictional requirements. For example, SEC Rule 17a-4 specifies retention periods for electronic records, emphasizing the importance of systematic archiving to ensure availability and integrity over time.

The denormalized 'Orders' table presented has multiple attributes which could potentially violate normalization principles, especially the third normal form (3NF). In its structure, the table contains fields like CustomerName, Street, City, State, and Zip alongside CustomerNum. The presence of CustomerName and address details alongside CustomerNum indicates redundancy and possible violation of 3NF, which stipulates that non-key attributes must be fully functionally dependent on the primary key and store only data related to the entity represented by the key.

In this case, the 'CustomerName' and address fields depend on CustomerNum but are repeated across multiple orders, creating redundancy. The 'Order' table would no longer be in 3NF because it contains transitive dependencies; for example, CustomerName, Street, City, State, and Zip depend on CustomerNum, not directly on OrderNum. This violates the third normal form, which requires that non-key attributes should not determine each other. The proper normalization would involve splitting the data into separate Customer and Order tables. The Customer table would contain CustomerNum, CustomerName, Street, City, State, and Zip, with each customer represented once, while the Order table would include OrderNum, OrderDate, CustomerNum, PartNum, Description, NumOrdered, and QuotedPrice.

The current table, including fields such as CustomerName, Street, City, State, and Zip alongside CustomerNum, is likely in the second normal form (2NF) but not in 3NF. The 2NF ensures the absence of partial dependency on a composite key, whereas 3NF eliminates transitive dependencies among non-key attributes. Therefore, to conform to 3NF, the database schema should be refactored into multiple related tables, decoupling the customer details from order details into separate entities.

Implementing normalization not only improves data consistency and reduces redundancy but also simplifies data maintenance and enhances integrity. For example, if a customer moves, updating their address in a dedicated Customer table automatically updates all orders linked through CustomerNum, eliminating inconsistent data entries.

References

  • Elmasri, R., & Navathe, S. B. (2015). Fundamentals of Database Systems. Pearson.
  • ISO/IEC 27001 Standard. (2013). Information security management.
  • United States Securities and Exchange Commission. (2018). SEC Rule 17a-4: Records and Preservation of Records. Retrieved from https://www.sec.gov/about/offices/ocie/ocie-recordkeeping
  • Gramm-Leach-Bliley Act, Pub. L. No. 106-102, 113 Stat. 1338 (1999).
  • SEC (Securities and Exchange Commission). (2020). Regulation S-P: Privacy of Consumer Financial Information. Retrieved from https://www.sec.gov
  • Dodd-Frank Wall Street Reform and Consumer Protection Act (2010).
  • Codd, E. F. (1970). A relational model of data for large shared data banks. Communications of the ACM, 13(6), 377-387.
  • Kimball, R., & Ross, M. (2016). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley.
  • Coronel, C., & Morris, S. (2015). Database Systems: Design, Implementation, & Management. Cengage Learning.

Related Assignments