Arvesta: Modernizing a fragmented BI landscape into a unified, scalable data platform
Databricks
Power BI
Azure
Arvesta is a Belgian full-service partner for the agricultural sector, with over 2,000 employees supporting farmers and growers across Europe. The company operates across the entire value chain, from animal feed and agriculture supplies to crop solutions and advisory services. Through its integrated approach, Arvesta helps agricultural businesses improve productivity, sustainability and profitability.
The challenge
Arvesta needed to modernize its fragmented BI landscape.
Their existing environment, built on Qlik and multiple on-prem SQL Servers, could no longer meet the growing need for real-time insights, centralized governance, and consistent data quality.
Data was scattered across diverse systems such as SAP S/4 & ECC, AS/400, Navision, Business Central, and IoT sensors.
The lack of a unified platform made it difficult to establish a single source of truth, enable self-service analytics, and support future ambitions in IoT, advanced analytics, and AI.
Our solution
Intellus designed and implemented a modern Azure Databricks-based data platform following the medallion lakehouse architecture, supported by a metadata-driven ingestion and transformation framework.
Through a structured delivery approach of workshops, blueprinting, architecture design, implementation, and knowledge transfer, we co-created a secure, governed platform tailored to Arvesta’s operational needs.
The architecture combines Azure Data Lake Gen2 (Delta format), Databricks for PySpark processing, Azure SQL DB for metadata, and Power BI/Qlik for analytics.
The business value
Arvesta now benefits from a unified, scalable data platform that acts as a single source of truth across all business domains: sales, manufacturing, logistics, and marketing.
Automated and traceable data pipelines replace manual processes, improving data quality, reliability, and governance.
With centrally managed metadata, secure networking, and reusable frameworks, reporting and analytics development is significantly accelerated while reducing maintenance overhead.
