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OpenSearch users increasingly need flexibility in how and where their data is stored - querying data in cloud object storage without re-ingestion, sharing datasets across analytics tools and engines, or migrating between platforms without costly ETL pipelines.
This session presents a technical deep dive into how OpenSearch can now support indexing and querying vendor-agnostic open formats natively with the new multi-engine composable architecture.
The presentation covers how the composable storage layer abstracts format-specific details while maintaining query performance. We'll explore schema management across formats, predicate pushdown to minimize data scanning, and how still existing indices like Lucene can act as layer on top of open formats as secondary indices for accelerated filtering.
Through real-world scenarios and querying production data alongside cold-tier archives in object storage, sharing datasets with other analytics engines, and leveraging specialized formats like Lance for vector workloads or Parquet / Vortex for high-compression analytics, we demonstrate how open format support delivers data portability, interoperability, and freedom from lock-in.
Arpit Bandejiya is a Software Development Engineer ll at AWS, working in Opensearch. Over the past three years, he has focused on Shard management, scalability and performance optimization. His interests lie in distributed systems and building high performance scalable infrastruc... Read More →
Software engineer in Amazon , OpenSearch maintainer and leads features on improving the performance of indexing and search. Interested in designing high performance distributed systems.
