Global investment manager modernizes data operations with an AWS Databricks lakehouse architecture
A global investment manager needed to unify fragmented data systems and streamline analytics across global investment teams
Global investment manager modernizes data operations with an AWS Databricks lakehouse architecture.
Client: A global investment manager known for its disciplined, research-driven approach sought to modernize its data infrastructure to support faster analytics and AI-enabled insights. Operating across multiple countries, the firm manages complex financial data requiring precision, compliance, and agility.
Legacy systems had limited their ability to scale analytics across investment teams. Data silos and manual processes hindered collaboration and slowed portfolio analysis, creating inefficiencies in reporting and governance.
To address these challenges, the firm partnered with Lumenalta to design a lakehouse architecture on AWS Databricks — one that could unify data management, strengthen governance, and accelerate analytics at enterprise scale.
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Challenge: The investment firm’s legacy systems were robust but fragmented, creating friction across investment and data science teams. These limitations constrained innovation and added operational risk.
- Disparate data sources across S3, Snowflake, PostgreSQL, and legacy databases reduced visibility.
- Manual aggregation processes slowed investment analysis and increased the risk of error.
- Limited self-service access created dependencies between analysts and engineering teams.
- Aging infrastructure restricted the use of advanced analytics and AI initiatives.
- Siloed governance delayed access approvals and complicated audit tracking.
- Legacy Python workflows suffered from long runtimes and limited scalability.
- The firm needed enterprise-grade migration without disrupting day-to-day investment operations.
To modernize successfully, they required a partner with both technical depth and regulatory awareness—one capable of delivering modernization securely, at scale, and with measurable results.
Solution: Lumenalta developed and executed a two-phase strategy to re-engineer the client’s data ecosystem using Databricks on AWS, establishing a single, governed foundation for analytics and AI.
Phase 1: Proof of Concept (12 weeks)
- Implemented Databricks Auto Loader to automate 200+ datasets in near real time.
- Introduced Delta Live Tables (DLT) to reduce pipeline errors by 40% and accelerate ETL deployment.
- Connected external Snowflake data via federated queries, cutting dashboard creation time by 60%.
- Deployed AI/BI Genie tools that empowered non-technical users to create 30+ dashboards.
- Applied GenAI-assisted automation for data quality and documentation, reducing manual effort by 50%.
- Established Unity Catalog for centralized governance, reducing access approval times by 70%.
Phase 2: Enterprise Rollout
Following the POC, Lumenalta migrated two critical business lines to the new architecture. R-based risk analysis systems were rebuilt in Databricks and Delta Lake, improving performance and scalability. The team also transitioned AWS EMR workloads to Databricks and DBT for unified orchestration and faster transformations.
Following the POC, Lumenalta migrated two critical business lines to the new architecture. R-based risk analysis systems were rebuilt in Databricks and Delta Lake, improving performance and scalability. The team also transitioned AWS EMR workloads to Databricks and DBT for unified orchestration and faster transformations.
Together, these efforts modernized the firm’s entire data management lifecycle—from ingestion and storage to analytics, AI, and governance—without disrupting ongoing investment operations.
Outcomes: The modernization has redefined how the firm manages, analyzes, and governs its global investment data:
- 60% faster dashboard creation using federated queries and self-service BI tools.
- 70% reduction in access approval time through centralized governance and automation.
- 45% increase in developer productivity from optimized workflows and automation.
- 50% less manual effort via GenAI-powered data quality checks and documentation.
- 90% satisfaction among non-technical teams using self-service analytics.
- Stronger governance and compliance, reducing operational risk in portfolio management.
- Foundation for AI-driven investment strategies and predictive analytics at enterprise scale.
With AWS Databricks at its center, the firm now operates a unified, AI-ready data platform that improves insight speed, strengthens governance, and empowers analysts to focus on higher-value investment decisions.
90%
Satisfaction among non-technical teams using self-service analytics
45%
increase in developer productivity from optimized workflows and automation
50%
less manual effort via GenAI-powered data quality checks and documentation
Ready to unify your data, accelerate analytics, and unlock AI-ready insights at scale?
