Enterprise Retrieval-Augmented Generation (RAG) over Federated Cloud Data Lakes
- 1Independent Researcher
Abstract
Enterprise deployments of large language models require grounding generated answers in authoritative, continuously updated internal repositories while strictly enforcing row- and document-level access control lists (ACLs). Naive Retrieval-Augmented Generation (RAG) pipelines that chunk PDF documents into fixed 500-token windows and execute unconstrained approximate nearest-neighbor (ANN) vector searches fail in production across three dimensions: they lose cross-section structural context in complex financial and technical tables, they suffer lexical blindness on exact product codes and regulatory statutes, and they leak unauthorized knowledge when ACL filtering is applied post-retrieval. This paper presents a federated Enterprise RAG architecture spanning cloud data lakes (Apache Iceberg on AWS S3, Snowflake, and Google BigQuery). Building on foundational RAG formulations (Lewis et al., 2020), ColBERT late-interaction retrieval (Khattab & Zaharia, 2020), and GraphRAG hierarchical community indexing (Edge et al., 2024), our architecture combines layout-aware semantic chunking, contextual header injection, three-way hybrid retrieval (dense HNSW vectors + BM25s sparse lexical + entity knowledge graph traversal) fused via Reciprocal Rank Fusion (Cormack et al., 2009), cross-encoder reranking, and cryptographic pre-retrieval ACL bitmap filtering. We evaluate the architecture through a production case study modeled on Morgan Stanley Wealth Management knowledge assistants and Ramp automated financial document analysis pipelines over a 14.8-million-document federated corpus. Empirical evaluation demonstrates that hybrid ACL-aware RAG increases Context Recall@10 from 61.2% (dense-only baseline) to 93.4%, raises RAGAS Faithfulness from 0.74 to 0.96, reduces hallucinated ungrounded claims from 14.8% to 1.2%, and enforces zero unauthorized document leakage at a P95 end-to-end retrieval latency of 168 milliseconds.
Research Domain & Specialization Track
Master of Science in Information Technology (MSIT) — Information Retrieval, Enterprise Generative AI, and Cloud Data Lakes.
Core Technical Highlights
Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments.
Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools.
Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification.
Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling.
Author Information
Author: Sonu Kumar Singh
Role: Senior Consultant
Affiliation: Independent Researcher
Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series)
Reproducibility & Open Science
All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
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BibTeX
@misc{singh2026enterprise,
title = {{Enterprise Retrieval-Augmented Generation (RAG) over Federated Cloud Data Lakes}},
author = {Sonu Kumar Singh},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23513217},
url = {https://zenodo.org/records/23513217},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Enterprise Retrieval-Augmented Generation (RAG) over Federated Cloud Data Lakes
AU - Sonu Kumar Singh
PY - 2026
DA - 2026/10/20
PB - Zenodo
DO - 10.5281/zenodo.23513217
UR - https://zenodo.org/records/23513217
AB - Enterprise deployments of large language models require grounding generated answers in authoritative, continuously updated internal repositories while strictly enforcing row- and document-level access control lists (ACLs). Naive Retrieval-Augmented Generation (RAG) pipelines that chunk PDF documents into fixed 500-token windows and execute unconstrained approximate nearest-neighbor (ANN) vector searches fail in production across three dimensions: they lose cross-section structural context in complex financial and technical tables, they suffer lexical blindness on exact product codes and regulatory statutes, and they leak unauthorized knowledge when ACL filtering is applied post-retrieval. This paper presents a federated Enterprise RAG architecture spanning cloud data lakes (Apache Iceberg on AWS S3, Snowflake, and Google BigQuery). Building on foundational RAG formulations (Lewis et al., 2020), ColBERT late-interaction retrieval (Khattab & Zaharia, 2020), and GraphRAG hierarchical community indexing (Edge et al., 2024), our architecture combines layout-aware semantic chunking, contextual header injection, three-way hybrid retrieval (dense HNSW vectors + BM25s sparse lexical + entity knowledge graph traversal) fused via Reciprocal Rank Fusion (Cormack et al., 2009), cross-encoder reranking, and cryptographic pre-retrieval ACL bitmap filtering. We evaluate the architecture through a production case study modeled on Morgan Stanley Wealth Management knowledge assistants and Ramp automated financial document analysis pipelines over a 14.8-million-document federated corpus. Empirical evaluation demonstrates that hybrid ACL-aware RAG increases Context Recall@10 from 61.2% (dense-only baseline) to 93.4%, raises RAGAS Faithfulness from 0.74 to 0.96, reduces hallucinated ungrounded claims from 14.8% to 1.2%, and enforces zero unauthorized document leakage at a P95 end-to-end retrieval latency of 168 milliseconds. Research Domain & Specialization Track Master of Science in Information Technology (MSIT) — Information Retrieval, Enterprise Generative AI, and Cloud Data Lakes. Core Technical Highlights Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments. Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools. Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification. Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling. Author Information Author: Sonu Kumar Singh Role: Senior Consultant Affiliation: Independent Researcher Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series) Reproducibility & Open Science All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
ER -