Executive Summary
The client, a prominent U.S.-based investment management firm, undertook a strategic modernization of its data architecture by transitioning from a legacy .NET and SQL Server-based framework to Databricks. The objective was to streamline data processing, consolidate disparate logic layers, and enhance performance and scalability while preserving data integrity. This migration supports their long-term vision of building a unified, cloud-native, and future-ready data platform.
Business Problem
The client operated a legacy data processing framework built on .NET and SQL Server that handled investment account data in a sequential and fragmented manner. The system lacked scalability, was resource-intensive, and made it difficult to maintain and scale complex business logic tied to specific categories of data. Additionally, siloed logic between .NET and SQL Server created operational inefficiencies, delayed processing times, and impeded the firm’s agility in adapting to evolving business needs. The overarching goal was to decommission this legacy ecosystem and consolidate operations within Databricks.
Solutions Implemented
- Migrated existing data processing systems from SQL Server and .NET to Databricks SQL.
- Reengineered 24 Data Producers under the targeted module, each with potentially unique datasets and business logic.
- Translated SQL stored procedures into Databricks SQL as the initial phase of migration.
- Transformed .NET logic into modular Databricks SQL constructs.
- Enabled bulk processing of client accounts simultaneously, replacing the earlier sequential approach in .NET.
- Validated the integrity of converted logic by benchmarking Databricks output against legacy SQL Server datasets.
- Centralized integration of SQL and .NET logic within Databricks to ensure cohesive management.
- Partitioned code across multiple views to enhance modularity, readability, and reuse.
Enabled selective data processing by targeting specific categories of data, improving performance and control.
Consolidated disparate .NET and SQL logic into a single Databricks environment, reducing system complexity
Enabled parallel processing of client accounts, significantly reducing processing time
Allowed differentiation of business logic across categories without duplicating code, enhancing maintainability.
Streamlined data operations by processing only relevant categories of accounts, cutting down on unnecessary computation costs.
Partitioned code architecture, improved development velocity and onboarding for new developers.
Positioned the client to sunset its legacy platforms, reducing technical debt and aligning with cloud-first transformation goals.
Technology Stack
