Product descriptions:
The Rank Group Plc (“Rank”) is an international gaming, leisure and entertainment company that operates casinos, bingo clubs and online gaming platforms in the United Kingdom, Spain and Portugal. Its brands include Grosvenor Casinos, Mecca Bingo and Enracha, with licenses in more than 100 communities. Rank’s data team struggled with data silos, quality issues, and high costs from managing multiple data sources. To address these challenges, the company moved to Databricks Lakeflow Jobs to bring together and effectively manage data from multiple sources directly into their Lakehouse, achieving over £1.2 million in cost savings and unlocking critical business insights and higher productivity.
The challenge of fragmented data across multiple data warehouses
Rank relies heavily on data across multiple critical business functions to ensure responsible gambling practices, safeguarding customers through careful monitoring and intervention. That same data powers their marketing activities and member benefit programs, enabling them to better understand high-value customers while improving the overall customer experience. Their goal was ambitious: to have all of their data at their fingertips with real-time access to KPIs that could drive faster, more informed decision-making across the organization.
However, achieving this vision was nearly impossible with their existing infrastructure. Data was fragmented across seven different data warehouses, creating silos that prevented effective data orchestration or a unified view of the business. The company managed about 50 systems with various data types: operational, customer information, Safer Gambling incidents, game, marketing, finance, and employee data. Many of these systems were homegrown, with some dating back 25 years. "Each system was generating its own KPIs, and they were not talking to each other," explained Sachin Wadhwa, Director of Data Architecture and Platforms.
Rank’s data team also had to deal with data quality issues, which were compounded by a lack of documentation and extremely high costs from maintaining multiple ETL systems. They recognized the need to address these challenges by consolidating everything into one place and simplifying their entire data architecture.
Orchestrating 20 million daily transactions with confidence
To address its fragmented data landscape, Rank chose Databricks Lakeflow Jobs to effectively manage and orchestrate all of their data, regardless of the type or source. The company uses Lakeflow to build a pattern-based framework that pulls data from multiple sources—including APIs, SQL databases, message queues and files—at varying frequencies throughout the day, depending on business needs. Once ingested, the data flows through bronze, silver and gold layers where it's cleaned, transformed and prepared for consumption by downstream applications, ML models and Power BI dashboards The scale is substantial: Lakeflow Jobs is able to process nearly 20 million transactions per day alone. "Everything is running on Lakeflow Jobs at this point," said Wadhwa.
The migration from legacy solutions to Lakeflow Jobs, which took approximately eight months, transformed how Rank manages its data operations. The team now runs over 150 jobs with 100+ executing daily in production, leveraging many of Lakeflow’s capabilities, including trigger-based workflows or Power BI tasks. This level of automation and orchestration was impossible with their previous Azure Data Factory setup, which lacked proper error handling and support mechanisms. Now, when a workflow fails, it automatically creates a service ticket and sends alerts via Microsoft Teams, enabling 24/7 monitoring and rapid response.
Lakeflow Jobs also delivers significant operational and cost benefits. Rank has adopted a strategic compute approach thanks to Lakeflow Jobs serverless processing. Serverless is used for their smaller, real-time datasets, offering faster startup times, easier management, simpler SQL-mode logs and better stability.
To effectively control costs—a major priority for the organization—Rank leveraged Lakeflow Jobs’ native observability and flexible reporting. They were able to easily build custom dashboards using system tables to centrally track job performance and failures, monitor usage patterns and maintain daily cost oversight through their ops team.
The result is a fully supported, reliable, end-to-end environment that gives Rank the real-time and unified data access, governance and cost control they envisioned.
Unlocking innovation with trusted, real-time customer data
The decision to consolidate their data orchestration and migrate to Lakeflow Jobs has delivered substantial cost savings and operational improvements across the entire organization. During each phase of the implementation, Rank was able to dramatically reduce data costs and ended up with a total of £1.2 million in savings.
By eliminating data silos and establishing a single, trusted source of high-quality data, Rank has fundamentally transformed how the business operates. Daily reporting now arrives an average of four hours earlier than before, enabling faster business decisions.
Data quality has also improved dramatically with Lakeflow observability. Monthly reports show that data quality rules now achieve a pass rate exceeding 97.2%—a substantial improvement over prior systems. Data operations engineers have been freed up by approximately eight days per month, improving their productivity by 30%, allowing them to focus on higher-value projects rather than troubleshooting data issues.
Thanks to Lakeflow Jobs, the data team gets access to faster, more granular data for predictive models around customer churn and value, supporting improved campaign analysis, advanced segmentation models and better customer targeting. Critically, the centralized orchestrated data helps Rank understand player habits to adhere to Safer Gambling requirements while handling their high transaction volumes without any performance degradation. "With Lakeflow Jobs, we were able to tap into data that legacy technologies could not access, empowering us to generate deeper, more reliable business insights," said Wadhwa.
The platform has also enhanced data governance and GDPR compliance while future-proofing the business for AI/ML and ongoing data-driven innovation. Rank is now working on a unified membership system that will provide a clear view of their customers and their gaming habits and preferences—a project that would have been impossible with their fragmented legacy systems. "We’ve been seeing lots of benefits using Databricks in terms of performance, error handling and the amount of data that we can process so easily," Wadhwa noted.
Ultimately, the combination of improved team productivity, collaboration and the ability to handle massive data volumes has positioned Rank to compete more effectively in the rapidly evolving gaming and entertainment industry.
As Simon Kaffel, Rank’s Chief Data Officer, says: “Our ability to harmonise fragmented datasets underpins our strategic vision to deliver trusted, actionable data seamlessly to our end users.”