For any iGaming operator, affiliate network, or platform provider, the data warehouse is the foundation of analytics, automation and AI driven decision making. At Gamblitude, we are fully Data Warehouse native, which means we adapt to your existing data architecture instead of forcing proprietary ingestion models.
Integration modes
There are multiple ways to integrate data with our Data Warehouse layer. Each approach comes with different trade offs in terms of latency, complexity and operational cost. Below we outline the four most common integration patterns we support.
Stream processing via Pub/Sub or Kafka
Stream processing is the preferred and most future proof integration method.
In this model, data is published as events to a message broker such as GCP Pub/Sub or Apache Kafka. Gamblitude subscribes to these topics and ingests events in near real time.
This approach works especially well for modern microservice based architectures, where systems already communicate via events.
Key benefits:
- Near real time data availability
- High resilience and fault tolerance
- Natural fit for event driven systems
- Easy horizontal scaling
Even if your current architecture does not emit events, streaming can still be achieved using Change Data Capture tools like Debezium. With the Outbox Pattern, database changes are safely converted into events without impacting transactional workloads.
This option is ideal for operators who want real time dashboards, instant anomaly detection and AI agents working on fresh data.
API based integration
API integration is a very common and flexible option.
Data is pushed to Gamblitude using HTTP or gRPC endpoints. This allows systems to send data directly from backend services, batch jobs, or third party tools.
Key benefits:
- Simple and well understood integration model
- Fast time to market
- Easy to secure and monitor
- Scales well with traffic
API ingestion works well when streaming infrastructure is not available or when data is generated in well defined requests, for example CRM events, affiliate conversions, or payment status updates.
Latency is typically low, but the model still depends on how often data is sent by the source system.
File exchange via object storage
In this model, data is delivered as files to object storage such as AWS S3, GCS Cloud Storage, or similar BLOB storage.
Gamblitude ingests data by scanning predefined folder structures. Using Hive style partitioning by date or other keys allows us to minimise storage API calls and optimise processing.
Key benefits:
- Low cost data transfer
- Simple integration for systems which doesn’t support streaming
- Very reliable for large volumes
- Easy to reprocess historical data
The main limitation is latency. File based ingestion usually introduces mini batching (and/or aggreagation of data), which means data is not available in strict real time. For many financial, reporting, or compliance use cases, this is perfectly acceptable.
Pull based integration and polling
In some cases, pushing data is not possible. For these scenarios, Gamblitude can fetch data directly.
We can pull data from:
- External APIs
- Read only SQL database replicas
Data is fetched incrementally using a watermark column such as updated_at or created_at.
Key characteristics:
- No changes required on the source system
- Suitable for legacy platforms
- Batch oriented by design
This approach also introduces batching and higher latency, but it is often the fastest way to integrate with older systems or third party providers.
Additional technical considerations
Regardless of the integration method, there are several principles that apply across all setups.
Data formats
We typically work with AVRO due to its strict schema enforcement, compact size and excellent compression. Schema based formats reduce ambiguity and make downstream analytics and AI more reliable. JSON format is also supported.
Event independence and deduplication
Our ETL layer is designed to handle independent events, duplicates and late arriving data. We support updates to existing rows and ensure idempotent processing across pipelines.
Security and observability
All data is encrypted in transit and handled according to strict security standards. Equally important is observability. We provide diagnostics, logging and monitoring to quickly identify schema issues, delivery gaps, or data quality problems.
Summary
Choosing the right integration model depends on your architecture, latency requirements and operational maturity. In practice, many operators use a hybrid approach, combining streaming for critical events and file or API ingestion for heavier datasets.
Gamblitude is built to support all of these patterns, so your data warehouse can evolve together with your business.
