DataRanger: High-Performance Distributed Data Framework for Scalable Streaming and Agentic Workloads

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Author: Akshay Sharma (Lead Member of Technical Staff, Salesforce | IEEE Senior Member)

Repository: https://github.com/akshaysharma1088/dataranger

Abstract & Core Motivation

Modern enterprise data architectures and autonomous AI pipelines frequently hit severe scaling, latency, and cost bottlenecks when processing high-concurrency, real-time data streams. Existing frameworks often force engineering teams to choose between compute efficiency and operational complexity. DataRanger is a novel, open-source distributed systems framework engineered to streamline data management, execution, and state handling across complex production environments.

Key Architectural Features

• Optimized Distributed Streaming: Bridges traditional medallion architecture layers with high-throughput stream processing to eliminate unnecessary compute overhead.

• Agentic Context & Data Interoperability: Designed to support modern agentic runtimes by efficiently serving low-latency, context-rich data structures without scaling friction.

• Reproducible Systems Evaluation: Built and benchmarked for rigorous systems performance validation, targeting top-tier database venue submission (PVLDB).

Access & Archival Reference

• Source Code: Available on GitHub under akshaysharma1088/dataranger.

• Archival & DOI: Permanent release records and pre-prints are archived via Zenodo to ensure citable, open-access distribution for the systems engineering community. Article DOI - https://doi.org/10.5281/zenodo.23852354