Challenges:

• Many tables have an excessive number of columns and lack a naming standard suggests inefficient data organization. This waste storage space makes it challenging to maintain data consistency.

GenAI Solutions:

• Database Optimization through Normalization – Auto identification and removal of redundant columns
• Splitting tables into focused structures with auto identified relationships and standardized column names.

Challenges:

• Cryptic or non-existent error messages cause crashes when invalid data is entered, disrupting user workflow, and requiring restarts.

GenAI Solutions:

• Automated Error Message Generation – Appropriate exception handling structures through auto recommendations
• Provided code snippets for specific error scenarios.
• Reviewed existing code for potential error handling weaknesses.

Challenges:

• The absence of a standardized naming convention for Master, Transaction, Mapping, and Number Series tables makes it challenging to understand the purpose and relationships between tables. This hinders maintainability, collaboration, and efficient querying.

GenAI Solutions:

• Craft Clear Naming Rules – Instant table purpose recognition through adding prefixes like “mst_” for master tables, “trx_” for transactions, etc
• To make column purpose crystal clear attached suffixes like “_id” for primary keys and “_date” for date columns
• Enabled Consistent Style (e.g., camelCase or snake case) and apply it to all tables and columns for a clean, unified look.

Challenges:

• Legacy databases suffer from slow queries due to inadequate indexing, including missing, outdated, and fragmented indexes, along with unused indices consuming disk space.

GenAI Solutions:

• Automated Index Analysis – Analyzed query patterns to pinpoint frequently accessed columns.
• Identified tables lacking indexes on those key columns.
• Compared existing indexes with current access patterns to flag outdated ones.

Challenges:

• User passwords are transmitted in plain text, allowing them to be intercepted and misused.

GenAI Solutions:

• Used Indexing – Implemented enhanced authentication to secure passwords and introduced RBAC for added security. This mirrors the security upgrades facilitated by Gen AI in modernization.

Challenges:

• Concurrent transactions become gridlocked. Each transaction holds a lock on a resource (data) needed by the other, preventing either from completing. This can lead to stalled processes and reduced database efficiency

GenAI Solutions:

• GEN AI tackles database deadlocks by addressing the underlying schema issues.
• Excessive dependencies between tables.
• Lack of proper normalization (reducing data redundancy

Challenges:

• Legacy database documentation often suffers from gaps (missing info), inconsistency (naming, format, etc.), and jargon overload. This creates a knowledge black hole for developers, hindering collaboration and leading to data integrity issues.

GenAI Solutions:

• Auto-categorized elements – Quickly find tables, columns, etc.
• Plain language explanations – Simplified complex database jargon.
• Standardizes documentation – Ensured everyone speaks the same “database language.

Challenges:

• Manually creating Entity-Relationship (ER) diagrams becomes a time-consuming burden for developers due to complex database schemas, repeatedly recreating diagrams in tools like draw.io, and risk introducing errors through manual data entry.

GenAI Solutions:

• Streamlined ER diagram creation by automating the process.
• Analyzed database tables and schema.
• Auto generated a draft ER diagram.
• Exported the diagram to tools like draw.io for further editing.