How a leading audio entertainment company modernized its data with a lakehouse

Summary

A leading audio entertainment company in North America, with a user base exceeding 100 million and over 67 million receiving personalized music, faced a growing crisis in its data infrastructure. Legacy systems were no longer capable of supporting the scale, complexity, and speed needed to deliver real-time insights and rich, personalized content experiences. Siloed data and batch-processing limitations were holding the organization back.

To regain agility and remain competitive, the company partnered with Marlabs to implement a cloud-native data lakehouse architecture powered by Databricks on AWS. This solution unified fragmented data systems, introduced real-time analytics capabilities, and enabled advanced machine learning workflows. Through a structured, six-phase implementation, the company laid a strong foundation for data-driven innovation, scalability, and long-term customer engagement.

Challenge

Legacy data infrastructure hindered understanding and ability to adapt to customers’ needs

The company’s rapid growth in users, platforms, and content types had outpaced the capabilities of its aging data infrastructure. Business-critical data was trapped in disconnected legacy systems, making it difficult to generate timely insights or coordinate across teams. This fragmentation introduced redundancies, slowed analysis, and made it impossible to perform real-time personalization at scale.

Reliance on batch data processing meant that by the time insights were available, they were often outdated. With customer expectations rising and digital competitors accelerating their innovation, the company needed to reimagine how it managed and utilized its data. The only path forward was to replace its legacy infrastructure with a unified, scalable, and intelligent data platform.

Solution

Design and implement a future-ready data lakehouse

To tackle its infrastructure challenges and position for future growth, the company engaged Marlabs to design and implement a modern lakehouse solution. The project unfolded across six deliberate phases, each designed to deliver incremental value while building toward a unified and intelligent data ecosystem.

Phase 1: Assessment & Planning

The first phase focused on understanding the current state of the company’s data landscape. Marlabs conducted a thorough audit of the client’s systems, workflows, and pain points. Working closely with internal teams, they defined the goals of the lakehouse project, prioritized business use cases, and developed a strategic roadmap that aligned technology with the company’s broader vision for innovation.

Phase 2: Design & Architecture

With a clear plan in place, the team moved to designing the lakehouse architecture. The design leveraged Delta Lake as the foundation and ensured compatibility with AWS infrastructure components like EC2, EMR, and S3. The architecture was built to support both streaming and batch data, accommodate varied analytics workloads, and provide the scalability required for long-term growth.

Phase 3: Implementation

Implementation began with consolidating disparate data sources into the new lakehouse platform. Real-time ingestion capabilities were enabled using modern streaming tools, while Databricks was configured to support SQL analytics, machine learning, and dashboarding. This phase delivered the infrastructure necessary to power on-demand insights and made high-quality data more accessible across the organization.

Phase 4: Migration & Integration

Next, legacy data was migrated into the new environment using carefully planned workflows to minimize business disruption. Both real-time and batch pipelines were integrated into the platform, allowing teams to access historical and live data within a single interface. The integration effort also included aligning security, governance, and compliance controls to protect data quality and user privacy.

Phase 5: Optimization & Automation

Once operational, the lakehouse platform was fine-tuned for performance and value generation. Real-time dashboards and KPIs were developed to provide business users with actionable insights. Predictive models and personalization algorithms were implemented to tailor experiences for users at scale. Automated monitoring and data quality checks were embedded to ensure trust and reliability in every analytic output.

Phase 6: Scalability & Future-Proofing

The final phase prepared the solution for long-term adaptability. An open architecture was adopted to support easy integration with future data sources, tools, and platforms. Strategic blueprints were created to guide future enhancements, including AI and machine learning initiatives. This ensured that the lakehouse would continue to deliver value and support innovation in a fast-evolving industry.

Results

Improved analytics, faster decisions, and deeper engagement

The new lakehouse platform dramatically enhanced the company’s ability to act on data. Real-time access to insights reduced decision-making cycles and empowered teams with timely, accurate information. Personalized content became more dynamic and responsive, improving the user experience for millions.

Operational efficiencies were gained by streamlining data workflows and eliminating silos. With machine learning models running at scale, the company was able to anticipate user needs, optimize performance, and increase revenue through more targeted offerings. The data transformation provided a high return on investment and created momentum for continued innovation.

Impact

A scalable, intelligent foundation for future innovation

The lakehouse implementation created lasting improvements across the organization. It enabled the company to shift from reactive to proactive decision-making and opened the door to new analytics-driven capabilities.

• Enabled real-time insights for faster, smarter decisions
• Delivered highly personalized content experiences at scale
• Increased operational efficiency through unified data workflows
• Enhanced data governance, security, and quality controls
• Supported long-term growth through open, cloud-native architecture
• Positioned the company to lead in AI-driven personalization and innovation

With a future-ready platform in place, the company now operates with greater agility, insight, and confidence—ready to evolve alongside its customers and the ever-changing digital entertainment landscape.