🔍 TL;DR
Learn how WaterBridge's 3-person engineering team uses TimescaleDB to ingest, monitor, and analyze up to 10,000 data points across 1,200+ miles of pipeline infrastructure. They initially compressed 14TB down to 700GB, and after 1.5 years of growth, what would have been 72.77TB of data is now just 3.9TB - all while maintaining zero-delay monitoring for critical leak detection.
🔗 Get the full break-down — How WaterBridge Uses TimescaleDB for Real-Time Data Consistency.
The Challenge: Real-Time Monitoring at Scale
WaterBridge faced a critical data problem.
WaterBridge manages water infrastructure for oil and gas operations. When their database couldn't handle 5,000-10,000 data points per second across their sensor network.
As a small team of just three people, they needed a solution that:
✓ Wouldn't require custom development
✓ Could handle 5-10K data points per second
✓ Would process data without delay
✓ Would maintain performance at scale
✓ Would fit within budget constraints
✓ Could provide real-time visualization
The Solution: TimescaleDB as a Data Historian
After evaluating several options including ClickHouse and Canary, the WaterBridge team selected TimescaleDB for its familiar PostgreSQL interface and superior real-time capabilities.
"The appeal of Timescale was that we only needed to communicate with PostgreSQL. Real-time ingestion is critical for us: the control room trends metrics in real time. Therefore, as soon as data comes in, it must be displayed on the screen without delay, regardless of query execution time."
From SQL Server to TimescaleDB
| Challenges | Why TimescaleDB Worked For Them |
|---|---|
| 📈 Massive Scale: Monitoring 1,200+ miles of pipeline with close to a million metrics | ✅ PostgreSQL Compatibility: Seamless integration with existing stack |
| ⏱️ Zero-Delay Required: Control room needs real-time data for leak detection | ✅ Real-Time Ingestion: Handles 10K data points/sec with instant display |
| 💾 Storage Crisis: SQL Server reaching 4TB tier limit at $12,000/month | ✅ Hypercore Compression: 95% reduction in storage needs (72TB → 3.9TB) |
| 🐌 Performance Issues: Slow trends and degrading query speed | ✅ Superior Query Performance: Zero lag for critical monitoring |
| 👨💻 Small Team: Just 3 people with limited custom development resources | ✅ Easy Implementation: No custom drivers or specialized APIs needed |
| 📊 Data Access: Other teams needed aggregated historical data | ✅ Continuous Aggregates: Automatic downsampling for different use cases |
"TimescaleDB is absolutely critical to our operations. The control room team relies on real-time data consistency, so data must be ingested and displayed immediately as it becomes available."
| Before TimescaleDB | After TimescaleDB + Hypercore |
|---|---|
| 14TB spread across three databases | 700GB across three databases |
| $12,000/month for basic trending | Significant cost reduction |
| Struggling to ingest 5-10K data points/second | Processing 5-10K data points/second with instant visualization |
| 4TB tier limit, no compression | 73TB of raw data compressed to 4TB |
| Query performance degrading | Zero query lag for 24/7 monitoring |
| Selective data filtering required | Full data capture across ~1M metrics |
| Limited control room trends with lag | Real-time monitoring for leak detection |
| No historical data access | Auto-downsampling for all team needs |
| Scaling would triple costs | Supporting ML and predictive maintenance |
If your use case demands real-time insights, like WaterBridge’s, try TimescaleDB. You can self-host it or try our managed PostgreSQL option, Timescale Cloud, for free. This will allow you to focus on your app—not your database.
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