A London-based financial services firm engaged Maestro Logic to modernise a legacy enterprise analytics platform built on on-premise SQL Server Analysis Services (SSAS) multidimensional cubes. While the existing solution supported critical reporting across several business units, it had become increasingly constrained by infrastructure scalability limits, complex cube processing cycles, and growing data volumes. The objective was to migrate the platform to a cloud-native architecture capable of supporting scalable analytics, near real-time data ingestion, and future advanced data workloads.
Maestro Logic designed and implemented a modern Lakehouse architecture using Azure Databricks as the core analytics engine. The platform was built on cloud-native storage and compute, separating data storage from processing to enable elastic scaling and cost optimisation. Historical data from the existing SSAS cube environment and associated relational sources was extracted, normalised, and ingested into the Lakehouse using structured bronze, silver, and gold data layers to support both raw ingestion and curated analytical datasets.
A distributed data ingestion framework was implemented using micro-batch processing to support both high-volume batch ingestion and streaming workloads. Operational data sources were integrated through incremental ingestion pipelines, enabling the transition from traditional scheduled ETL processes to near real-time data processing. Databricks structured streaming and micro-batching techniques were used to efficiently process incoming datasets while maintaining transactional consistency and schema governance within the Lakehouse environment.
The analytical layer was redesigned to replace cube-based aggregations with scalable distributed transformations and optimised data models within Databricks. This approach enabled improved query performance, reduced processing latency, and simplified data pipeline orchestration. The platform also introduced enhanced observability, automated pipeline monitoring, and improved data lineage to support governance and operational reliability.
By migrating from a monolithic SSAS cube architecture to a cloud-native Azure Databricks Lakehouse platform, the organisation significantly improved the scalability, resilience, and flexibility of its analytics environment. The new architecture enables the business to support both traditional reporting workloads and modern data applications, including advanced analytics and machine learning, while reducing operational complexity and infrastructure overhead.