01 · Hour-lag warehouse updates
Operational updates took about an hour to reach the analytical warehouse. Reducing the delay through the existing platform would have increased cost, while long SQL procedures were difficult to maintain and optimize.
Customer work · Logistics
A logistics platform moved operational changes into BigQuery in near real time
BluePi built a Google Cloud MVP that captured changes from MySQL, PostgreSQL, and MongoDB with Debezium Server. Optimized BigQuery procedures made current operational data available for reporting at a lower cost and shorter runtime.
Operational data reached the warehouse sooner
The existing warehouse path made near-real-time data expensive to provide. Complex SQL procedures had also accumulated over time, which increased runtime and made repeated optimization difficult. BluePi used a bounded MVP to test a lower-cost CDC and BigQuery path.
02 · CDC and BigQuery path
Debezium Server captured changes from MySQL, PostgreSQL, and MongoDB. BigQuery stored the updated data, and optimized procedures applied the merge and transformation logic required by reporting teams.
03 · Current data with lower processing cost
The MVP established near-real-time CDC availability in BigQuery. Procedure optimization improved runtime, native BigQuery capabilities reduced cost, and a reusable merge procedure reduced repeated development and testing work.
From a source change to a current warehouse record
Debezium captured changes from MySQL, PostgreSQL, and MongoDB, BigQuery stored the current records, and optimized procedures applied the merge and transformation logic required by reporting teams.
Capture each source change
Debezium Server read database changes instead of repeatedly extracting complete tables. This reduced the work required to keep BigQuery current.
Standardize the merge
A reusable merge procedure applied a consistent update pattern across tables. Development, testing, and later optimization followed the same implementation.
Make failure visible
Alerts identified the stage where data stopped moving. The team could respond to a source, CDC, procedure, or warehouse problem without treating every freshness issue as the same incident.
Where this pattern fits
This pattern fits operations teams that need fresher warehouse data but face rising platform cost or long-running transformation procedures. A useful first step is a bounded set of tables and one critical procedure with measurable latency, runtime, and cost baselines.
Case details
Open a section to review the customer problem, implementation, business change, and architecture.
01The starting point
The logistics platform needed operational updates to reach reporting sooner and at a sustainable cost.
- Hourly data delay: Booking and delivery changes were not visible quickly enough for current operational reporting.
- Rising platform cost: Increasing freshness through the existing warehouse would have raised infrastructure cost.
- Long SQL procedures: Transformation logic had grown across several developers and was difficult to optimize.
- Repeated merge work: Each table required similar development, testing, and tuning.
- Limited failure visibility: Teams needed to know which stage caused a delayed update.
02What BluePi built
BluePi implemented a bounded CDC and BigQuery MVP.
- Debezium Server: Captured changes from MySQL, PostgreSQL, and MongoDB.
- BigQuery landing: Received source changes for analytical use.
- Optimized procedures: Critical merge and transformation logic was rewritten for BigQuery.
- Reusable merge procedure: New tables followed one standardized update pattern.
- Pipeline alerts: Failures were visible at important points in the data path.
- Performance measurement: The MVP compared freshness, procedure runtime, and cost with the existing platform.
03Results
The MVP demonstrated that operational changes could reach BigQuery in near real time while reducing warehouse cost and procedure runtime.
- Near-real-time CDC: Operational changes became available in the warehouse sooner.
- Improved procedure performance: Critical transformation logic completed faster.
- Lower platform cost: BigQuery and optimized procedures reduced the cost of the tested path.
- Less repeated engineering work: The reusable merge procedure reduced development, testing, and optimization effort for additional tables.
- Visible failures: Alerts identified where a source-to-warehouse update stopped.