Company and Use Case
Trebellar provides a platform for real estate management, streamlining data collection and analysis for large commercial properties. Their focus is on normalizing data from IoT sensors (such as temperature, occupancy, and air quality) to help building managers optimize their operations. By integrating diverse data streams into a centralized platform, Trebellar helps clients make informed decisions about their space utilization and building efficiency.

Performance Problems to Solve
Trebellar was facing significant challenges in processing large volumes of time-series data generated from their customers' IoT devices. They needed a solution that could handle the high frequency of data inputs, normalize it efficiently, and support real-time insights for their platform's machine learning models. The complexity of managing data across different devices, formats, and locations made it difficult to provide actionable insights in a timely manner.

Performance Gains Unlocked
By adopting TimescaleDB, Trebellar significantly improved their ability to manage and query large datasets. With features like time-bucketing, compression, and seamless integration with PostgreSQL, Timescale allowed them to downsample and compress data effectively, reducing storage costs by 50%. They can now provide real-time analytics and insights to their customers, enhancing decision-making for building management and optimizing energy usage, occupancy, and air quality monitoring.