Utilizing Real-Time Transit Data for Travel Optimization

Explore how to optimize travel using real-time transit data in this 42-minute talk focused on the MTA, New York City's public transportation system. Learn to leverage public and semi-public data feeds to navigate efficiently, avoid delays, and minimize expenses in urban environments. Discover how to implement a data-driven decision-making process for travel planning using Apache NiFi for data ingestion and transformation, Flink SQL for streaming analytics, and various Apache projects including Kafka, OpenNLP, Tika, Avro, Parquet, and Iceberg. Gain insights into creating a streamlined system that delivers travel recommendations via Slack messages, while maintaining data visibility through NiFi lineage, Kafka topic views, and Iceberg tables. Apply these techniques to improve your commute and extend the concepts to other metropolitan areas worldwide.

Utilizing Real-Time Transit Data for Travel Optimization