4B Rows Daily, Sub-Second Queries
4Global provides sports and leisure analytics. They process billions of rows of participation data daily—sports facility usage, membership records, activity tracking across global markets.
Their problem: 8-hour pipeline runs. Slow queries. Storage costs scaling linearly with data volume. They needed faster processing, cheaper storage, and sub-second query response.
The Challenge
Multiple Data Sources
Facility management systems, membership databases, activity tracking APIs—all generating terabytes of data with no central platform.
Slow Pipelines
8-hour ETL runs meant stale data. Dashboards updated once daily. Analysts waited hours for query results.
Cost Scaling
Storage costs growing 1:1 with data volume. No compression. No tiering. Infrastructure bill scaling linearly.
The Solution
Datazone replaced 4Global's existing ETL pipeline and data warehouse. Single lakehouse for all sports and leisure data. Distributed query engine. Columnar storage with automatic compression.
Data Integration
Facility systems, membership DBs, activity APIs—all ingested into one lakehouse. Single schema for all sports data.
Distributed processing handles billions of rows per day. Partitioned by date and region for query performance.
Incremental updates from source systems. No full table scans. Change data capture for real-time sync.
ML Pipeline
Churn prediction, usage forecasting, facility demand models— trained directly on lakehouse data. No data export required.
Models deployed as versioned endpoints. A/B testing built in. Rollback to previous versions in seconds.
Real-time predictions served at query time. Batch scoring for historical analysis. Same engine, same data.
Query Performance
Columnar storage + partition pruning. Dashboards load in under 1 second across billions of rows.
Automatic columnar compression. 9x reduction in storage costs. No data quality loss.
8-hour ETL reduced to 30 minutes. 16x faster. Data refreshes throughout the day instead of nightly.
What Changed
4Global processes 4 billion rows daily with sub-second queries. 16x faster pipelines. 9x cheaper storage. ML models train on fresh data instead of day-old snapshots.
4Global replaced an 8-hour nightly ETL with 30-minute incremental updates. Storage costs dropped 9x through columnar compression. Queries that took minutes now finish in under a second. ML models train on current data instead of yesterday's snapshot. Same team, same data volume—far lower cost, far better performance.