About the project
This project was delivered by Digital Factory, MOL Group's digital transformation organization supporting the Retail business. Operating across 10 Central European markets, the Retail division manages approximately 2,300 service stations and convenience stores, serving millions of customers every day.
MOL Group is one of Central Europe's leading integrated energy companies, headquartered in Budapest, with operations in more than 30 countries and approximately 25,000 employees. As business demand for data-driven decision-making continued to grow, Digital Factory set out to modernize its analytics landscape with a platform that could support both current business needs and future AI ambitions.
What was the challenge?
Digital Factory previously relied on an on-premises, SQL-based data warehouse that primarily supported business intelligence reporting using large volumes of transactional and customer data. While the platform was stable and reliable, it was not designed to support advanced analytics, machine learning, or modern data engineering workflows.
To meet the growing demand for deeper insights and advanced analytical capabilities, Digital Factory introduced a cloud-based analytics architecture combining Databricks and Azure Synapse. Databricks was primarily used for data engineering, development, and machine learning, while Azure Synapse served SQL-based analytical workloads.
Although this approach expanded the organization's capabilities, it also introduced a fragmented technology landscape. Developers and analysts worked across separate platforms, increasing operational complexity, infrastructure costs, and administrative overhead. Maintaining two environments also created inconsistencies in performance, governance, and user experience, while limiting scalability for future data and AI initiatives.
To support the next stage of its data journey, Digital Factory needed a unified cloud-based platform that would simplify operations, improve performance, reduce costs, and provide a scalable foundation for analytics, data engineering, and AI.
What was the solution?
As Databricks SQL Warehouse evolved into a mature analytics solution, Digital Factory identified an opportunity to consolidate both development and SQL analytics onto a single platform without compromising performance or functionality.
An initial assessment demonstrated significantly better query performance and substantially lower operating costs compared to Azure Synapse. Based on these findings, Digital Factory decided to migrate all SQL workloads from Azure Synapse to Databricks in collaboration with its implementation partner, Abylon. The migration enabled both developers and analysts to work within a shared environment while allowing existing SQL code to be reused with minimal modifications.
The migration covered SQL queries, reporting workloads, and user workflows. Once the transition was complete, Azure Synapse was successfully decommissioned, leaving Databricks as the organization's unified platform for data engineering, analytics, and machine learning.
A key component of the solution was Databricks SQL Warehouse, which enables users to execute high-performance SQL queries directly within Databricks while seamlessly connecting to reporting tools such as Power BI through native connectors. This allowed existing reports to continue operating with minimal redevelopment effort while significantly simplifying the overall architecture.
Today, Digital Factory operates a more unified analytics platform powered by Databricks. Consolidating development, data processing, and analytical workloads into a single environment has simplified platform operations, improved governance, strengthened collaboration across data teams,and established a scalable foundation for future analytics and AI initiatives.
Key results after the Databricks implementation
The new platform provides Digital Factory with a more efficient, scalable, and cost-effective foundation for data-driven decision-making. By simplifying the components of the central data platform,the organization reduced operational costs while improving performance and accelerating access to business insights.
Key achievements
