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Letting Data Speak, AI Act!
Case Study
Enterprise Cybersecurity Platform Modernization
About the Client
A cybersecurity and compliance platform serving enterprise clients across multiple industry sectors. The company provided security monitoring and compliance solutions to organizations requiring robust data protection and regulatory adherence. As a multi-tenant SaaS platform, the client served diverse enterprise customers with varying compliance requirements and security monitoring needs, making this case study applicable to similar cybersecurity vendors, compliance platforms, and enterprise SaaS providers undergoing infrastructure modernization.
Challenge
The client faced a critical infrastructure migration challenge that threatened their ability to scale and maintain competitive service delivery:
● Legacy Infrastructure Limitations: The existing tech stack and infrastructure had reached capacity limits and could not support the growing customer base or new feature requirements demanded by the competitive cybersecurity market.
● Complex Multi-Tenant Architecture: Multiple tenant databases operated with different schemas, creating data silos and inconsistent data structures that hindered operational efficiency and feature development across the platform.
● Fragmented Data Storage: Data was scattered across various databases and storage systems, making it nearly impossible to provide unified analytics, comprehensive reporting, or centralized compliance monitoring that enterprise clients required.
● Zero-Downtime Migration Requirement: As a mission-critical cybersecurity platform, the client could not afford service interruptions during migration, requiring both old and new systems to operate simultaneously while maintaining perfect data synchronization.
● Missing Analytics Infrastructure: The absence of a centralized data warehouse prevented the delivery of advanced analytics dashboards and historical reporting capabilities that were essential for enterprise cybersecurity and compliance use cases.
Key Results
Optimized database architecture by 94%, reducing total tables from 700 to 40 while maintaining full functionality and improving query performance.
Achieved seamless zero-downtime migration
Enhanced analytics capabilities resulted in faster compliance reporting and dashboard generation
Solution
The migration strategy employed a comprehensive approach that ensured business continuity while modernizing the entire data infrastructure:
● Database Schema Redesign and Optimization: The PostgreSQL schema was designed from scratch to eliminate redundancy and integrate data from multiple legacy sources. This comprehensive redesign consolidated and optimized the database structure, reducing the total number of tables from 700 to 40 while maintaining full functionality and improving query performance.
● Historical Data Migration Strategy: Full load scripts were developed to migrate all historical data from legacy MySQL databases and Elasticsearch sources to Databricks, followed by structured ingestion into PostgreSQL. This approach preserved years of critical security and compliance data while establishing the foundation for the new architecture.
● Real-Time Data Synchronization: AWS Database Migration Service (DMS) was implemented to capture incremental changes from MySQL databases and store them in S3. Custom Change Data Capture (CDC) pipelines were developed to process this incremental data and maintain synchronization between Databricks and PostgreSQL during the parallel operation period.
● Streaming Data Processing Infrastructure: A dedicated streaming pipeline was built using AWS Kinesis to handle real-time device monitoring events. The Databricks streaming pipeline processed and ingested this continuous data flow, performing upsert operations into the PostgreSQL database to maintain current security monitoring states.
● Analytics and Reporting Platform: Amazon QuickSight was integrated as the primary analytics dashboard solution, querying Databricks directly to provide real-time cybersecurity insights and compliance reporting. Additional DMS configurations synchronized data from PostgreSQL to S3, feeding specialized Databricks pipelines that transformed data specifically for QuickSight consumption.
● Data Warehouse Architecture: A centralized data warehouse was established in Databricks to store historical data, support advanced analytics, and enable comprehensive compliance reporting across all tenant environments.
Technologies Used
Amazon Cloud Service
AWS Database Migration Service (DMS)
Databricks
PostgreSQL
AWS Kinesis
Amazon QuickSight
AWS S3
MySQL
Elasticsearch
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