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This case study describes the final phase of a strategic data platform migration aimed at building a robust, scalable, and trustworthy data foundation for future AI adoption.
This case study describes the final phase of a strategic data platform migration aimed at building a robust, scalable, and trustworthy data foundation for future AI adoption.
The client’s traditional BI environment supported regular reporting, but the underlying data architecture, processing patterns, and platform limitations created a growing AI/BI gap. This migration was positioned as a prerequisite for AI transformation, focusing on removing structural barriers that would otherwise make future AI initiatives fragile, expensive, and high-risk.
The client is an international B2B company providing specialized, high value-added ingredients to manufacturers in the food and beverage industry. Its business model is built on strong R&D capabilities, continuous innovation, and long-term partnerships with FMCG clients. Data plays a critical role in supporting commercial decision-making, operational efficiency, and future analytics initiatives across the organization.
Prior to the migration, the client had a functioning BI environment that supported sales reporting and operational dashboards. Business users relied on these insights to monitor performance and guide decisions, and adoption of analytics across the organization was steadily increasing.
However, the data platform was never designed to support advanced analytics or AI-driven use cases. Data processing logic was fragmented, transformations were hard-coded, and data ingestion patterns were optimized for reporting rather than analytical scalability. As a result, any attempt to move beyond descriptive BI toward predictive or prescriptive analytics would have introduced significant operational risk.
This situation represents a classic AI/BI gap: analytics outputs existed, but the underlying data architecture lacked the standardization, reproducibility, and scalability required to support AI reliably.
Several structural challenges prevented the existing platform from serving as a foundation for AI and advanced analytics:
Data processing was split across two separate ELT frameworks. The legacy framework relied on undocumented, hard-coded logic unsuitable for reuse. A newer, metadata-driven framework had been introduced, but the migration was halted following Microsoft’s announcement to sunset Azure Synapse support.
Analysis of the legacy pipelines revealed inefficient compute usage, with workloads requiring sustained capacity of up to 3,000 DWUs for several hours daily. Monthly infrastructure costs exceeded $11,000, driven by architecture patterns that were neither scalable nor cost-efficient.
The platform was built on Azure Synapse, a technology approaching end-of-support. Continuing to invest in this architecture would have increased long-term risk and limited the organization’s ability to adopt modern analytics and AI capabilities.
SAP ECC data ingestion was dominated by full historical loads, often pulling more than 15 years of data per run. Some datasets were so large that they could only be processed once per month over the weekend, with the longest load exceeding 17 hours.
We joined the project to lead the strategic migration of the legacy environment. During this phase, we also contributed to architectural refinements, orchestration improvements, and the adoption of standardized development practices aligned with long-term analytics and AI scalability.
The objective of the new platform was not to implement AI directly, but to design a data architecture capable of supporting AI workloads in the future. This required consistent transformations, transparent dependencies, scalable compute, and reliable orchestration - capabilities that were missing from the legacy setup.
The resulting platform was built on three core technologies: Databricks, dbt, and Azure Data Factory.
Databricks served as the core data processing engine, providing flexible compute and high-performance processing capabilities. This enabled the platform to scale with growing data volumes and workloads while ensuring long-term platform support and alignment with Microsoft's strategic data ecosystem.
DBT (Data Build Tool) established a standardized, reusable, and well-documented development framework. Reusable logic was encapsulated in dbt macros, enabling consistent handling of incremental and full data loads across models, as well as automated generation of standard attributes such as timestamps and primary keys. This approach reduced duplication of effort, simplified maintenance, and ensured consistent development patterns.
Azure Data Factory acts as the orchestration layer, providing centralized scheduling, monitoring, and dependency management. Tumbling window triggers dynamically manage dependencies between ingestion and transformation layers, creating a foundation suitable for more complex analytical pipelines in the future.
SAP Data Ingestion Redesign: SAP ECC ingestion was restructured by organizing extracts into logical datasets rather than single, monolithic loads. For large-volume objects, delta loading was introduced. This reduced the longest load time from over 17 hours to approximately 5 hours and enabled daily processing instead of monthly batch runs.
Standardized Development Patterns: The adoption of dbt introduced consistent transformation logic, reusable macros, and clearly defined dependencies. This significantly improved maintainability and created deterministic, reproducible data transformations, a critical requirement for advanced analytics and AI use cases.
Compute Optimization: By consolidating processing onto a single Databricks-based architecture and optimizing workload execution, the platform achieved substantial performance improvements while reducing overall infrastructure costs.
Self-Service Analytics: The new Enterprise Data Model enables self-service analytics for over 300 employees, a capability that did not exist in the legacy platform. Approximately 50 users actively work with migrated reports, with the model serving as a centralized analytical layer for broader organizational use.
This implementation marks the completion of the client’s data platform migration and the starting point of its AI transformation journey. By addressing the AI/BI gap at the architectural level, the organization significantly reduced the risk of future AI initiatives and established a scalable, future-ready data backbone.
This foundation positions the client to move confidently from descriptive analytics toward predictive, prescriptive, and AI-driven decision-making, when the business is ready to take the next step.
