Building a cloud-native Smart Data Platform to support real-time gaming operations

Key takeaways

• A fragmented data environment was replaced with a centralised AWS-based Data Lake architecture.
• Near real-time data delivery enabled faster operational and product decisions.
• A scalable, serverless API layer allowed secure data sharing across internal teams and external partners.

Grand Parade, operating in the gaming and gambling sector, required a modern data analytics foundation capable of supporting high transaction volumes and dynamic business activity. Existing data warehouse structures were no longer sufficient to serve as a single source of truth for the organisation.

Gaming platforms generate large volumes of behavioural, transactional, and event data. Delays in processing or inconsistent access to this information can directly affect user experience, marketing effectiveness, and operational oversight. The organisation therefore set out to build a Smart Data Platform that would serve as the central data backbone for analytics, reporting, and integration with internal and external systems.

The objective was not only to modernise infrastructure, but to create a platform that could scale with business growth and adapt to fluctuating traffic patterns.

The challenge

The transformation involved multiple layers of complexity.

• The existing data warehouse environment required assessment and rationalisation.
• The new platform had to operate fully in AWS while remaining cost-efficient.
• Batch processing and streaming pipelines had to coexist within the same architecture.
• Data needed to be shared securely across internal teams and third parties.
• The platform had to support 24/7 gaming operations, requiring monitoring and incident response mechanisms.

Additionally, business demand required increasingly near real-time data access, placing pressure on traditional batch-driven architectures.

Solution overview

The project began with a detailed analysis of the existing data warehouse landscape and a technology assessment. A proof of concept was created to validate architectural decisions before full implementation.

The resulting Smart Data Platform was built on AWS with Snowflake as the core analytical engine. The architecture combined batch data processing and streaming capabilities to support both historical analysis and near real-time insights.

The platform included:

• an AWS-based Data Lake foundation,
• Snowflake for scalable analytical workloads,
• Apache Airflow for orchestration of batch processes,
• Kafka for streaming data pipelines,
• Terraform for infrastructure as code,
• CI/CD automation for controlled deployment,
• a serverless API layer using AWS API Gateway and Lambda for data sharing,
• monitoring systems for selected Data Lake components,
• technical documentation (LLD) and 24/7 failure-response plans.

The design ensured that the platform could support both large analytical projects and smaller ad hoc initiatives without structural changes.

How the work was executed

The engagement covered technology evaluation, architecture design, and implementation. Infrastructure was provisioned using Terraform to ensure repeatability and consistency across environments.

Batch processing pipelines were orchestrated with Airflow, while Kafka enabled streaming data ingestion for scenarios requiring faster insight delivery. Snowflake provided scalable storage and compute separation, allowing performance adjustments based on workload intensity.

A serverless API layer was implemented to expose selected datasets securely to internal teams and third parties. This reduced dependency on manual exports and simplified integration scenarios.

Monitoring and alerting mechanisms were configured to support continuous platform operation. Documentation and incident-response procedures were prepared to enable 24/7 support teams to act quickly in case of disruption.

As real-time analytics needs increased, streaming delivery options were expanded to reduce latency between event occurrence and analytical availability.

Operational results

The Smart Data Platform became the central data source for the organisation.

Key outcomes included:

• implementation of a scalable Data Lake based on AWS and Snowflake,
• near real-time data delivery for selected operational use cases,
• a secure, serverless API access layer for data distribution,
• monitoring systems covering critical Data Lake components,
• structured support mechanisms for gaming platform data operations.

The platform now supports both operational decision-making and strategic analytics across the organisation.

Business value

The new architecture reduced infrastructure costs through elastic cloud scaling and separation of storage and compute resources. Near real-time data availability improved responsiveness in marketing, risk management, and product optimisation.

The ability to adapt capacity to changing business activity levels is particularly valuable in the gaming sector, where traffic can fluctuate significantly. At the same time, structured access controls and serverless integration improved data security and governance.

By consolidating data into a unified, cloud-based platform, Grand Parade gained a scalable and cost-efficient foundation capable of supporting both current analytics needs and future expansion.

By implementing a cloud-native Smart Data Platform, Grand Parade achieved scalable, near real-time analytics while reducing costs and strengthening control over data access and security.