TL;DR:
Trebellar manages 10M daily IoT sensor readings from commercial buildings while cutting storage costs by half. Their engineering team accomplished this with just "five lines of code" by implementing TimescaleDB with PostgreSQL, enabling both real-time analytics and efficient data compression for their customers in retail, hospitality, and storage properties.
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Providing Real-Time Insights From Millions of IoT Sensor Readings Daily
Trebellar provides a platform for real estate management, streamlining data collection and analysis for large commercial properties. They transform data from IoT sensors (such as temperature, humidity, occupancy, and air quality) into insights, helping building managers optimize their operations and make informed decisions about their space utilization and building efficiency.
A Three-Layer Pipeline
The Trebellar team built a pipeline that ingests and normalizes sensor data from any source to monitor and predict building efficiency, eliminating silos in building management data. There are three layers within that pipeline:
The Data Layer: Collecting and normalizing sensor data from diverse sources
The Insights Layer: Applying machine learning for predictive analytics
The Action Layer: Generating solutions based on the processed insights
Performance Challenges
Trebellar was facing challenges in processing large volumes of time-series data generated from their customers' IoT devices. They needed a solution that could:
Handle massive volumes of time-series data at high frequency
Normalize inputs from diverse device types and formats
Support real-time analytics for machine learning models
Manage data across different devices, formats, and locations
These challenges directly impacted Trebellar's ability to deliver on their promise of actionable, timely insights for their customers.
Why PostgreSQL? The Gold Standard Solution
The Trebellar engineering team had always liked PostgreSQL as a battle-tested, open-source database option. However, they needed a way to optimize it specifically for their demanding, time-series workloads.
They chose Timescale not only for the power of TimescaleDB and specialized time-series functionality, but also for the seamless integration with PostgreSQL, a familiar ecosystem they already trusted.
"We capture 10 million points, 10 million rows a day. We need to downsample that after a month and compress it. I can do that so seamlessly with essentially five lines of code. If Timescale didn't exist, perhaps we would have tried to just do something directly with PostgreSQL, but that would have resulted in much worse performance.”
— David, Co-Founder, Trebellar
The Results: 50% Storage Reduction and Real-Time Capabilities
With TimescaleDB, Trebellar significantly improved their ability to manage and query large datasets. With the Timescale automation framework and features like hypertables, time-bucketing, and compression, Timescale enabled them to downsample and compress data effectively, reducing storage costs by 50%.
Storage costs cut in half through intelligent compression and downsampling
Seamless data lifecycle management with minimal code
Enhance query performance for both historical and real-time data
Real-time insights to end users for immediate decision-making
The Impact: Transforming Building Management
Now, Trebellar can provide real-time analytics and insights to their customers, streamlining decision-making for building management and optimizing energy usage, occupancy, and air quality monitoring. Trebellar’s platform enables their clients to effectively:
Monitor and optimize energy usage in real-time
Track and analyze building occupancy patterns
Maintain optimal environmental conditions
Make data-driven decisions about space utilization
By normalizing diverse data streams into actionable intelligence, Trebellar is helping transform commercial real estate management from reactive to proactive—all while operating at a scale of 10 million data points daily.
PostgreSQL for Everything
Trebellar's case exemplifies our belief in PostgreSQL as a solution for diverse data challenges and demanding workloads. By integrating TimescaleDB with PostgreSQL, they gained specialized time-series capabilities while keeping all the reliability and ecosystem advantages they already trusted.
This technology choice lets them focus on their core mission of delivering real-time property insights, without needing a custom database solution from scratch.
Trebellar is one of many companies building better applications with PostgreSQL on Timescale, but there are many more to celebrate, like our friends at Nocodelytics, SolarNetwork, and Sentinel Marine Solutions.
Beyond Technology: The Community Factor
Trebellar's decision wasn't based solely on technical specifications. As David, Co-Founder of Trebellar, noted, it was "the quality of the documentation, community, content, and more" that solidified their choice.
This highlights an often-overlooked aspect of technology decisions: the ecosystem surrounding a solution can be as important as the solution itself.
If you would like to try Timescale free for 30 days, sign up here or self-install TimescaleDB on your machine.
check out these resources you may find helpful.
- Timescale Documentation
- Best Practices for Scaling PostgreSQL
- Why You Should Use PostgreSQL for Industrial IoT Data
- Storing IoT Data
- Real-Time Analytics Benchmark: Timescale vs the Competition
What's Your Story?
Do you have a PostgreSQL implementation you're proud of? We'd love to hear how you're using it to solve real business problems. Leave a comment!
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