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How Hopthru powers real-time transit analytics from a 1 TB table
- Hopthru leverages TimescaleDB to enable real-time transit analytics from a 1 TB table, transforming queries from minutes to seconds.
- Their three-person team uses continuous aggregates and tiered storage for powerful visualizations to optimize public transportation networks efficiently.
- Hopthru utilizes time-series data to analyze and optimize transit networks, aiding agencies in understanding route efficiency.
- The challenge lies in processing massive Automated Passenger Counting (APC) data efficiently for valuable insights.
- Hopthru's data pipeline includes data ingestion from buses' sensors, database queries, and storage processes.
- TimescaleDB significantly improved query performance over traditional setups for real-time analytics.
- Their tech stack includes Python, Django, PostgreSQL, TimescaleDB, and Redis for data management and analytics.
- Continuous aggregates and tiered storage strategies contribute to reducing query times and managing costs effectively.
- Hopthru plans to develop 'Hopthru Cleanse' for processing raw sensor data and ensuring data accuracy for National Transit Database certification.
- Lessons for development teams include choosing tools wisely, leveraging managed services, designing for access patterns, and optimizing through monitoring and continuous improvement.
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