Read-time current-state: how ISK deduplicates CDC on Iceberg
Diving into the mechanism behind ISK: how it transforms a Kafka CDC stream into a deduplicated, current-state Iceberg table that any Iceberg-compatible engine can query.
The freshness gap: uniting real-time and batch analytics
Modern data architectures promise the best of both worlds: real-time insight from streaming platforms like Kafka and deep historical analysis from open table formats like Apache Iceberg. This post explores the freshness gap - the architectural divide that forces organisations to choose between fresh data and complete historical context
The cost structure of near-real-time CDC analytics
Near-real-time CDC analytics costs are driven far more by continuously reconciling and maintaining current-state tables than by data volume itself, and Streambased argues that decoupling freshness from commit cadence can reduce those costs by an order of magnitude while simplifying architecture.
.avif)
.avif)
