Challeneges and Solutions involved in DB MODERNIZATION

Challenges

Reason

GEN-AI Solution

Stuck in Slow Motion
Manually analyzing stored procedures written in an obscure SQL dialect hampers progress and innovation. Additionally, lacking primary and foreign key constraints causes ambiguity in data identification and retrieval.
  • Leveraging prompt engineering with Gen AI accelerator significantly reduced the time required for analyzing stored procedures, identifying tables, and establishing relationships between them. This automates the previously tedious manual analysis process.
Wasted Storage & Inconsistent Data
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.
Database Optimization through Normalization
  • Auto identification and removal of redundant columns
  • Splitting tables into focused structures with auto identified relationships and standardized column names.
Poor Error Handling
Cryptic or non-existent error messages cause crashes when invalid data is entered, disrupting user workflow, and requiring restarts. 
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.
Inconsistent Naming Conventions
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.
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.
The Indexing Gap
Legacy databases suffer from slow queries due to inadequate indexing, including missing, outdated, and fragmented indexes, along with unused indices consuming disk space.
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.
Security Enhancements
​User passwords are transmitted in plain text, allowing them to be intercepted and misused. 
  • 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.
Database Deadlock
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
GEN AI tackles database deadlocks by addressing the underlying schema issues.
  • Excessive dependencies between tables.
  • Lack of proper normalization (reducing data redundancy)
Undocumented Database
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.
  • Auto-categorized elements: Quickly find tables, columns, etc.
  • Plain language explanations: Simplified complex database jargon.
  • Standardizes documentation: Ensured everyone speaks the same "database language."
Er diagram in documentation
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.
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.

Breaking Barriers: Exploring Business Impacts in Traditional and Gen AI-Powered Modernization Paths

In the ever-evolving landscape of digital transformation, legacy modernization used to be a slow, difficult journey. Imagine wading through months of manual data analysis and database restructuring — a process riddled with delays and potential errors. Thankfully, those days are fading fast. The dawn of Generative AI (Gen AI) has ushered in a new era of accelerated modernization, streamlining the journey and empowering developers like never before.

Gen AI acts as your intelligent co-pilot, automating tedious tasks and ensuring data quality. With Gen AI, the modernization process becomes smoother, faster, and far more efficient.

Let’s dive deeper and explore the transformative impact of Gen AI on modernization, both from a business and technical perspective.

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