Vertica to Snowflake migration accelerated query response times by 60%.
Pythian audited the legacy cluster and executed a seamless migration to Snowflake on AWS.
Fast global expansion pushed the gaming studio’s legacy Vertica platform past its breaking point—causing multi-hour telemetry delays that blocked real-time fraud detection and player monetization. To eliminate performance bottlenecks and remove vendor risk, the studio partnered with Pythian for a zero-downtime platform migration. Our team mapped all analytical dependencies, refactored custom C++ functions, modernized streaming pipelines, and redeployed machine learning models straight into Snowpark ML. Today, game engineers deploy new player analytics in hours, and data teams run self-service queries without waiting on DBAs.
By partnering with Pythian, we eliminated our telemetry bottlenecks and transformed our legacy data estate into an elastic, self-service cloud asset."
Chief Technology Officer
Global Gaming Studio
$1.8M
Annual infrastructure savings
60%
Reduction in query latency
99.9%
Platform availability post-migration
Modernize your telemetry architecture to accelerate player analytics and AI.
Global player expansion pushed the studio's legacy architecture past its limit.
Pythian audited 200+ projections, refactored custom C++ functions, and migrated 800M daily player events to Snowflake on AWS with zero gameplay disruption.
When millions of daily active players are on the line, you don't get second chances at a migration. Pythian modernized our entire architecture under the hood, eliminated our performance bottlenecks, and did it all without a single frame rate drop or moment of downtime for our players."
Chief Technology Officer
Global Gaming Studio
Pending acquisitions threatened platform support
Repeated ownership changes triggered governance reviews regarding support continuity and roadmap confidence for mission-critical player SLA reporting.
Manual tuning created engineering bottlenecks
Over 200 hand-tuned projections and custom C++ UDx functions required constant DBA intervention for every new telemetry query pattern.
Ingestion bottlenecks delayed cheat detection
Legacy COPY batch loads and Kafka Scheduler pipelines struggled to process 800 million daily log events without latency spikes.
Rigid infrastructure prevented model deployment
Unoptimized resource pools restricted local VerticaPy models, causing 20% excess hardware provisioning and stalling matchmaking AI models.
Pythian stabilized telemetry pipelines to safeguard player SLA reporting.
Pythian audited cluster configurations, projection designs, resource pools, and Tuple Mover health. Our team mapped every projection directly to its underlying query pattern, keeping operations stable while setting up a clean migration plan. Live game reporting ran smoothly without interruption during initial code updates.
Engineers refactored legacy SQL to eliminate dashboard bottlenecks.
Our team converted Vertica-specific code, including TIMESERIES and MATCH clauses, into native Snowflake models. The engineering team completely rewrote 35 C++ and Python UDx functions into Snowflake-compatible UDFs. Code updates prevented performance drops across all major player analytics dashboards.
Modernized ingestion workflows to eliminate manual maintenance.
The engagement team replaced the legacy Kafka Scheduler setup with Apache Airflow, Fivetran, and dbt. The new ingestion system streams 800 million daily events directly into Snowflake staging tables. Modernizing the pipeline removed manual maintenance work and sped up data flow for live operations teams.
Deployed native ML models to drive automated scaling.
Pythian engineers rebuilt 14 legacy VerticaPy models on Snowpark ML to support automated server capacity planning and matchmaking analytics. Running inference workloads directly inside Snowflake cut excess hardware spend from 20% to under 5%. The studio now deploys real-time AI tools straight into production.
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