Video by Open Data Science and AI Conference via YouTube
Real-time analytics systems need to be fast but speed alone is not enough. They also need to be correct, consistent, and reliable under real production workloads.
In this ODSC AI East 2026 session, Deep Patel, Senior Data Engineer at Robinhood, explores how to build event-time-consistent analytics pipelines using Apache Kafka, Apache Flink, and Apache Pinot.
The session covers the challenges that often cause metric drift in streaming and OLAP systems, including late-arriving events, replay, backpressure, ingestion delays, and multi-tenant workloads. Deep introduces architectural patterns for exactly-once analytics, event-time watermarks, deterministic segment assignment, idempotent upserts, and Flink checkpoint alignment.
Watch to learn practical strategies for building low-latency analytics pipelines that deliver fresh, trustworthy insights at scale.
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