Leveraging Open Table Formats for Scalable and Flexible Data Infrastructure

Ravi Rane, Pooja Mulik

Abstract: This article offers a thoughtful deep-dive into one of the most practical challenges in data engineering today: building scalable, reliable, and agile data systems without compromising affordability or openness. The author moves beyond the usual surface-level praise of Open Table Formats (OTFs) to reveal how their architectural finesse through ACID compliance, schema evolution, and metadata optimization solves long-standing limitations of traditional data lakes. What stands out is the way Apache Iceberg, Delta Lake and Apache Hudi are presented not as abstract tools, but as enablers of real-world, high-performance analytics, handling everything from Time Travel queries to streaming ingestion and decentralized data ownership. It is evident that the narrative doesn't just list features, but connects them meaningfully to broader patterns like the lakehouse model and data mesh compatibility. This suggests that the evolution of OTFs is not just technical but philosophical, redefining how organizations approach data reliability at scale. That said, the piece could have drawn stronger parallels to industry case studies, yet its detailed walkthrough of transactional mechanics and storage abstractions compensated well. Overall, the work reads like a well-informed practitioner?s guide, blending conceptual clarity with architectural pragmatism.

Keywords: Open Table Format, Query Optimization, Delta Lake, Apache Spark