Uncovering Hidden Connections: Revolutionizing Database Modernization with GenAI-Powered Insights

Author: Praveen Kumar ([email protected])

Date: Nov 25, 2024

1. Introduction

In today's fast-evolving tech landscape, understanding and managing database dependencies can be the key to seamless digital transformation.

Imagine instantly mapping the web of connections in your databases, from hidden dependencies to key relationships, all through a few simple questions. This innovative solution combines automated scanning, advanced filtering, and vivid visualizations to make complex database structures accessible. With a GenAI-enhanced web app and a ChatGPT-like interface, users can dive deep into impact analysis and database insights—just by asking in plain English. It’s like having a data detective on call, ready to uncover dependencies and answer any question you throw its way!

Modern relational databases often contain hundreds of interrelated tables, views, stored procedures, and functions, posing a challenge for solution architects, developers, and business users to understand data flow and object dependencies.

By visualizing these relationships using Neo4j, it enables a deeper understanding of the "Social Network" of database objects.

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2. Challenges in the Database Modernization Journey

When we started exploring the use of "GenAI for Database Modernization," we identified recurring challenges that make the process complex, time-consuming, and costly:

• How can we understand the database design and architecture effectively?
• How can we uncover the implementation details of existing databases?
• How can we gather critical knowledge about dependencies and relationships without relying on a human expert?
• How can we automate the translation of database objects and queries to align with the target platform or stack?
• Can we extract meaningful insights from the database, such as domains, subdomains, and functional groupings?
• How can we ensure data behavior or query performance differences between the old and new systems are intentional and well-documented?
• How do we make the transition and cutover process as seamless and low-risk as possible?
• How can we minimize risks during database modernization by creating or enhancing automated tests as a safety net?

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3. Solution Overview

This modular and scalable solution provides a robust framework for database dependency analysis, documentation, and exploration. It seamlessly integrates Azure OpenAI for intelligent document generation & Text2Cypher conversion and Neo4j for dependency visualization and querying.

It simplifies the complex process of managing dependencies. Leveraging cutting-edge technologies and custom-built applications, it provides an intuitive, end-to-end framework for database architects, developers, and business users.

3.1 High-Level Solution Flow

Here is the detail of the high-level solution flow in analyzing database dependencies in this solution –

• Select Scanning Options: Users select entry points (Database List or Custom SP File) via the application UI for dependency analysis.
• Metadata Reading: Reads database object metadata to gather reference information on tables, views, and stored procedures.
• Dependency Scanning: Performs a deep scan to identify cross-database object dependencies and relationships.
• AI Documentation: Uses Azure OpenAI to generate detailed documentation and flow diagrams of dependencies.
• Neo4j Transformation: Converts scanned data into nodes and relationships for Neo4j compatibility.
• Data Export: Exports dependencies as Excel files (manual use) and Neo4j import files (graph database use).
• Graph Database Integration: Publishes dependency data to Neo4j AuraDB for advanced querying and visualization.
• DBGraphXplore Web App: Enables exploration and analysis of database dependencies within Neo4j.
• Supporting Components: Includes data loaders, configuration management, and export tools for flexibility and scalability.

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3.2 Key Components

3.2.1 Dependency Generation App [Custom build C# Application for Data Extraction, Transformation, and Loading (ETL) SQL Metadata]

To streamline the ETL process, I developed a C# application that automates metadata extraction, transformation, and loading into Neo4j:

• Metadata Extraction: A C# application automates the process of querying SQL Server to retrieve metadata about tables, views, stored procedures, and their interdependencies.
• Data Transformation: The extracted data is restructured to fit Neo4j’s graph database model, organizing database objects as nodes and dependencies as edges.
• Data Loading into Neo4j: The transformed data is uploaded into Neo4j, creating a live dependency graph that is always accurate and ready for analysis.

This automation reduces manual efforts and ensures that the Neo4j dependency graph is consistently accurate and up-to-date.

3.2.2 Knowledge Graph (Dependency Mapping) using Neo4j

Neo4j’s graph-based structure is particularly well-suited for mapping object dependencies and data flow in databases. Some benefits include -

• Efficient Querying: Neo4j’s Cypher language can efficiently capture and retrieve intricate relationship patterns.
• Intuitive Visualization: Visualizing objects as nodes and relationships as edges allows both technical and business users to understand dependencies at a glance.
• Scalability: Neo4j handles large datasets and complex relationships, making it ideal for enterprise-level databases.

Object Graph Schema

Here is the neo4j database schema, and it is still evolving –

The diagram represents a Neo4j Object Graph Schema that outlines the relationships between various database entities and an application. Here's the explanation of the schema components:

Nodes (Entities) : Each circle represents a node, which is a key object type in the database and application ecosystem. The primary nodes include:

• Database: Represents the overall database structure containing all dependent objects.
• Application: Acts as a central entity linked to the database objects it interacts with.
• Procedure: Refers to stored procedures within the database.
• Function: Represents database functions for performing specific operations.
• View: Indicates database views, which are virtual tables created by SQL queries.
• Table: Represents physical database tables where data is stored.
• Trigger: Refers to database triggers that execute actions based on events.
• Index: Represents indexes used to optimize data retrieval.
• Column: Refers to individual table columns or attributes.
• Key Constraint: Represents primary keys, foreign keys, or unique constraints in the database.
• Type: Refers to custom data types defined in the database.

Relationships: The lines represent relationships between nodes define how entities interact or depend on one another:

• Database → CONTAINS → Tables, Views, Procedures, Functions, Triggers, Types: Represents that the database holds these components as part of its structure.
• Application → Database: Shows the application interacting with a specific database.
• Application → Procedures, Functions, Views, Tables: Indicates the application uses or depends on these database components.
• Table → Columns, Key Constraints, Indexes: Highlights how tables are defined with specific attributes and optimization structures.
• Triggers, Types: Connect to relevant tables or other entities (relationships may be implied if not directly shown).

By modeling these relationships in a graph database, users can better understand and manage complex database and application dependencies.

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Data Visualisation [Using Neo4j’s Object Graph Explorer]

Object graph explorer is the neo4j’s inbuilt tool to query, explore, and visualize the nodes and related data in network object graph format. To query the data it requires a good understanding of the Cypher language (neo4j language to query data).

Example: In this example, I want to retrieve a sample of stored procedure dependencies and capture each one as a sequence in a connected path.

MATCH p =(:Database)-[:CONTAINS]->(:StoredProcedure)-[:DEPENDS_ON*8]->(:Table) RETURN p ;        

?The above query retrieves the longest chain of dependency calls within the database(s), starting from a stored procedure and cascading through other stored procedures until it reaches a table.

This query helps predict the dependency hierarchy within the database, identifying how stored procedures are interconnected and how they ultimately affect database objects like tables. By analyzing the longest dependency call chain, it can:

• Identify Critical Dependencies: Highlights key stored procedures and their relationships with tables, helping assess impact during changes or migrations.
• Optimize Query Performance: Predicts data flow and potential bottlenecks in stored procedures, improving database performance.
• Risk Assessment: Anticipates cascading effects of changes, allowing for better planning and minimizing disruption during updates.
• Database Maintenance & Refactoring: Identifies paths for optimization, and streamlining dependencies for more efficient database modernization and maintenance.

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3.2.3 DBGraphXplore Web App [Interactive Querying Using Azure OpenAI and Streamlit]

The DBGraphXplore web application makes dependency analysis accessible to a broader audience, facilitating collaboration between technical and business users and supporting database-related decision-making.

• Built using Python and Streamlit, this app integrates with Azure OpenAI (GPT-4o model) to translate natural language queries into Cypher commands.
• The app provides real-time results, displayed in an interactive and user-friendly interface.
• Users can ask questions like "What are the dependencies of Procedure X?" or "Show all downstream dependencies of Table Y."?

Some sample queries –

• List the top 10 stored procedure names with the maximum number of dependencies on the tables; provide the table count and database name in the tabular format.
• List the top 5 most used tables by stored procedures along with the database names in the tabular format.
• List 10 unused table names along with the database names in the tabular format.
• The most complex stored procedure name? and explain why
• List all the tables used by “XXX_GetXXXXSummary” stored procedure.
• List of top 10 tables with maximum views?
• Get all the stored procedures using the “PersonName” table. and return only the stored procedure names.
• List all the objects using the “XXXXItinerary_DCSXXX_XXX” stored procedure, and list the name and type.
• List all the database names.?

4. Benefits of the Solution:

This solution is a game-changer for organizations looking to modernize their databases efficiently and effectively, ensuring that the journey from legacy systems to future-ready platforms is smooth and low-risk.

• Improved Data Flow Understanding: Visualizes database dependencies, enabling both technical and non-technical users to grasp data relationships quickly.
• Efficiency Gains: Automation of metadata extraction and dependency mapping reduces manual effort and ensures accuracy.
• Enhanced Collaboration: Non-technical users can easily query database dependencies through a simple, natural language interface, fostering collaboration across teams.
• Risk Reduction: The solution minimizes errors by providing a complete, automated view of dependencies, ensuring no critical relationships are missed.
• Scalable and Future-Ready: Built on Neo4j, the solution can handle large, complex databases, making it ideal for enterprise-level applications.
• Faster Decision-Making: Real-time insights from the system enable quicker, more informed decisions in database management and modernization.

These benefits help organizations streamline their database modernization efforts, reduce risks, and improve overall decision-making.?

5. Future Scope and Innovations

The journey of database modernization doesn’t stop here. As technologies and business needs evolve, so too must the tools we use. The potential enhancements for this GenAI-powered approach include:

• AI-Powered Recommendations: Leverage Neo4j's data science algorithms, such as PageRank, Community Detection, and Shortest Path, to analyze database dependency graphs. For example, use PageRank to identify critical database objects with the highest dependency impact, enabling efficient impact analysis during schema changes. Similarly, apply Community Detection to group-related objects for modular refactoring, and use Shortest Path algorithms to trace dependency chains between entities for quick troubleshooting. This will enhance insights, automation, and decision-making in database dependency management.
• Cross-Platform Compatibility: Extending the application to support additional database platforms like PostgreSQL, Oracle, and MongoDB.
• Real-Time Monitoring and Alerts: Integrating live database monitoring to provide real-time alerts for dependency changes or performance anomalies.
• Advanced Visualization Techniques: Incorporating AR/VR for immersive dependency graph exploration, ideal for large enterprises.
• Enhanced Security: Ensuring data privacy and compliance with AI-driven risk detection mechanisms.

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6. Conclusion

Neo4j, coupled with tailored C# and Python applications, brings relational database management to the next level by making dependency analysis visual, interactive, and scalable. The C# ETL application ensures the database graph is always current, while the Streamlit-based interface powered by Azure OpenAI enables seamless natural language querying. This solution empowers architects, developers, and business users to collaborate effectively, making database dependency management faster, more intuitive, and more accessible than ever.