The foundation of Gamblitude
Data Warehouse
for iGaming.
Every event. A shared foundation.
A clearer view of your business.
Bring casino and sportsbook data together in a managed cloud lakehouse. Keep the detail behind your numbers, align your KPI definitions and give analytics, AI and predictive models a common starting point.
One operation. One data foundation.
Connect the data.
Keep the context.
An iGaming data warehouse brings player, betting, gaming and transaction data into a common analytical model. Gamblitude combines that model with event-level storage, so teams can work from shared definitions and still investigate the activity behind a result.
Bring your sources together.
Connect bets, game sessions, payments, bonuses and acquisition data through the ingestion methods that fit your operation.
Give the numbers one meaning.
Use a governed iGaming model and shared KPI definitions across dashboards, analysis and team conversations.
Go back to the individual event.
Preserve granular history for player analysis, historical comparisons and the development of predictive models.
How the data warehouse works
From your source systems
to your next question.
A connected architecture gives raw events a useful role in the business. Explore the four layers, from ingestion to the tools your teams use every day.
Ingest
Connect the systems you already run.
Bring operational and historical data into the platform through agreed connectors and delivery methods.
Retain
Keep the event, not only the total.
The lakehouse retains granular history in an iGaming data model, preserving the context that a fixed aggregate can hide.
Define
Make every KPI traceable to its definition.
The metric layer applies shared business definitions so teams can compare results on the same basis.
Use
Put the foundation to work.
Support dashboards, AI-assisted analysis, proactive investigations and operator-specific predictive models.
Data freshness depends on the source, ingestion method and agreed processing schedule. Streaming and scheduled ingestion can serve different needs within the same operation.
The value of event-level data
The total tells you what.
The detail helps explain why.
A daily deposit total is useful. The sequence of a player’s sessions, deposits and bets gives you more to investigate. Keeping individual events makes it possible to revisit history when the question changes.
For data, analytics & BI teams- Session startsProduct and channel context
- Deposit recordedPayment method and transaction status
- Bet placedStake, market and event timestamp
- Bet settledOutcome connected to the original bet
The sequence stays available.
So can the next question.
What the foundation powers
One model of the business.
More ways to understand it.
The data warehouse is the foundation beneath the Gamblitude platform. Each layer uses the data in a different way, from a question you ask to an investigation the agent starts.
Business Intelligence
Explore dashboards, Metrics, player Attributes and Segments. Use Insight Radar for recurring checks defined by your team.
Ask questions with AI AgentAutonomous Agent
Let the agent select and develop analytical investigations across your business data. Review the findings and supporting context, then decide how to respond.
Included in standard BI pricing. Your team retains ownership of business actions.
Predictive Models
Your history.
Your players.
Your models.
Each machine learning model is built individually on the operator’s historical data, around the outcome or decision it needs to support.
Explore all six applicationsPredictive Models are priced separately. Bonus Value Optimisation, Content Recommendations and RG Risk Detection are individually scoped implementations. RG assessment supports a separate player-protection process.
Governance built into the foundation
Sensitive data.
Considered controls.
Player and transaction data need clear boundaries. Hosting, access and information security are part of the data architecture.
Explore security at GamblitudeISO/IEC 27001
An information security management system covering the platform and its operating practices.
Controlled cloud environment
Google Cloud hosting with logical separation between clients. Review region and deployment requirements with the team.
Access and traceability
Role-based access controls and audit trails support accountable use of the data.
Before you connect
The practical
questions.
Start with your systems, your data and the decisions you want to support.
What is an iGaming data warehouse?
An iGaming data warehouse combines data from betting, casino, player, payment and other operational systems in a common analytical model. Gamblitude uses a cloud lakehouse to retain event-level history and provide shared data and definitions for analytics, AI and predictive modelling.
Which data sources can Gamblitude connect?
Ingestion supports APIs, secure file uploads, Kafka and cloud event streams. The integration is scoped around the systems and data you have, including relevant player, betting, gaming, payment, bonus and acquisition records.
How fresh is the data?
Streaming pipelines can process incoming events continuously. Actual freshness depends on source availability, delivery method and the processing schedule agreed for each use case. Historical imports and scheduled feeds can coexist with streaming data.
Can we bring historical data or migrate from an existing warehouse?
Yes. Historical data can be assessed and imported alongside ongoing feeds. We review the available granularity, identifiers, definitions and coverage before agreeing the migration or backfill scope.
How does the warehouse support AI and predictive models?
Shared data and metric definitions give AI analysis a consistent context. Event-level history also supports operator-specific model development. Autonomous Agent develops analytical investigations; predictive models estimate agreed outcomes or produce recommendations. Your team defines how results are used.
Are predictive models included with the data warehouse?
Predictive Models are separately priced. The data warehouse provides the shared foundation; model scope, historical data requirements and delivery are agreed for the implementation. Autonomous Agent is included in standard BI pricing.
How long does implementation take?
Timing depends on source access, data quality, historical coverage and the integration scope. The first step is to review your systems and priority use case, then agree the ingestion, validation and rollout plan.
Start with the foundation
Your data has more to tell you.
Bring it together.
Show us your current stack and the questions your teams need to answer. We’ll help define a practical path from source data to insight.