DeltaStream

DeltaStream is a unified serverless stream processing platform that integrates with streaming storage services - it acts as the compute layer on top of streaming storage. It provides functionalities of streaming analytics (stream processing) and streaming databases along with additional features to provide a complete platform to manage, process, secure and share streaming data.

DeltaStream provides a SQL based interface where you can easily create stream processing applications such as streaming pipelines, materialized views, microservices and many more. It has a pluggable processing engine and currently uses Apache Flink as its primary stream processing engine. DeltaStream brings relational database concepts to the data streaming world, including namespacing and role based access control enabling secure access and the ability to process and share streaming data regardless of where they are stored.

DeltaStream is designed to solve multiple issues:

• Cost Optimization - Modern data platforms like Snowflake and Databricks weren’t designed for high-frequency, always-on workloads. Batch pipelines reprocess entire datasets—even when only a few rows change—leading to lag, bloated compute bills, and stale data. DeltaStream solves this by shifting workloads from batch to streaming. It processes only the data that changes, in the moment it arrives, using familiar SQL. No orchestration tools, no complex glue code, no wasted cycles.
• Batch to Streaming - Legacy batch pipelines weren’t designed for today’s always-on data needs. They’re slow to update, painful to maintain, and block innovation. Whether while feeding dashboards, powering AI models, or syncing downstream systems—hourly refreshes and brittle orchestration simply aren’t fast enough. DeltaStream lets users shift from batch to real-time. Users can build continuous data pipelines using SQL, eliminate complex handoffs, and deliver insights in seconds, not hours.
• Agentic AI - Keeps LLMs, agents, and applications grounded with continuously updated materialized views and streaming tables. Can trigger real-time actions and inferences by monitoring events and applying streaming transformations on the fly. Reduces LLM spend by filtering unnecessary calls and batching only the high-value ones.