Snowflake Real Time Analytics for a Crypto Casino

About Client

The client is one of the world’s largest crypto casinos, both in terms of revenue and player volume. Operating in a highly competitive and fast-evolving environment means that leadership depends on quick, reliable insights to respond to fluctuations in player activity, market volatility, promotions, and risk exposure.

Challenge

The organization was relying on MySQL as its primary operational datastore, a system that had served them for years but no longer met their analytical needs. Operational reports were delayed by 24 to 48 hours, creating a significant lag between what was happening inside the platform and what leadership could see.

For a crypto casino, this delay was unacceptable. Decisions involving player bonuses, liquidity management, fraud monitoring, and campaign performance required insight within minutes—not days.

The core challenge was clear: move from a legacy batch-driven model to a modern Snowflake real time analytics ecosystem without disrupting ongoing operations. At the same time, the client team needed clarity on the right visualization integration—particularly as they compared tableau vs Snowflake for handling large volumes of live data and ensuring fast dashboard refresh rates.

The business wanted real time reporting Tableau dashboards that were reliable, responsive, and did not overwhelm compute resources.

Our Solution

We designed a near real-time data pipeline centered on Snowflake, AWS services, and Tableau. The solution delivered a balance of speed, stability, and scalability without compromising data quality or system performance.

1. Continuous Data Capture from MySQL

AWS Database Migration Service (DMS) was used for Change Data Capture (CDC), enabling continuous replication of MySQL tables. This ensured up-to-the-minute updates without placing any additional load on the production database.

2. AWS S3 as the Staging Layer

Data flowed into Amazon S3 as the staging layer. While the client initially considered Snowpipe, its current limitations around continuous ingestion made S3 the more reliable and cost-efficient choice. The S3 → Snowflake pattern also ensured that data remained clean, versioned, and traceable.

3. Snowflake as the ODS and Analytics Warehouse

Snowflake received processed data via AWS Lambda triggers, enabling near real-time ingestion. This architecture ensured performance even as volumes scaled.

Summary tables were created for core KPIs—player activity, deposits, withdrawals, wagers, revenue, session behaviour—so that analytical queries and Tableau extracts could run against optimized, pre-computed datasets.

4. Tableau for Reporting and Visualization

Tableau dashboards were configured to refresh frequently from Snowflake summary tables, ensuring quick load times even with large volumes of streaming data. This addressed the client’s questions around real time reporting Tableau performance and provided a clear comparison regarding tableau vs Snowflake capabilities for different workloads.

Outcome

The result was a powerful and scalable near real-time analytics ecosystem built on:

  • AWS DMS for continuous ingestion
  • AWS S3 as the staging layer
  • Snowflake as the ODS and warehousing engine
  • AWS Lambda for automated processing
  • Tableau for intuitive reporting and instant insights

The impact was immediate:

  1. Decision-making accelerated dramatically. Leaders no longer waited 24–48 hours; insights were refreshed within minutes.
  2. Snowflake real time analytics enabled operational agility, helping teams adjust promotions, detect anomalies, and optimize liquidity in near real time.
  3. Summary tables reduced compute costs by cutting down unnecessary queries and ensuring efficient Snowflake utilization.
  4. Tableau dashboards refreshed instantly, thanks to optimized Snowflake structures, resolving previous limitations around real time reporting Tableau workloads.
  5. The business now had clarity around tableau vs Snowflake roles—Snowflake for data processing power and Tableau for visualization and exploration.

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