Senior Data Architect @ AWS Professional Services

_{Also known as: Vaquar Khan | Viquar Khan}

AWS | GCP | AZURE | PCF | Microservices | Big Data | Apache Spark | GenAI & Agentic AI | ML/AI SME | Polyglot Developer | Architect | Technology Evangelist

Va_ⁱquar Khan - Senior Data Architect at AWS Professional Services with 22+ years of expertise in finance and data analytics. I empower global financial institutions to harness the full potential of AWS technologies by designing cutting-edge, customized data solutions tailored to complex industry needs.

As a polyglot developer skilled in Java, Scala, Python, and other languages, I specialize in large-scale distributed systems, cloud architecture, big data development, Generative AI & Agentic AI solutions using Amazon Bedrock, and AWS AI/ML solutions for highly competitive enterprise clients. Ranked in the top 2% on both GitHub and Stack Overflow worldwide.

• JSR 368 Expert Group Member: Shaped industry standards for Java™ Message Service 2.1 — JCP Nomination · JSR-369
• Apache Spark Committer (since 2013): 12+ year commitment — voted on release candidates since Spark 1.1.1 (2014) and Spark 1.2.0 (dev@ mailing list archive)
• AWS AI/ML Expert: Designing intelligent data solutions with AWS AI services
• GenAI & Agentic AI SME: Architecting solutions with Amazon Bedrock, Bedrock Agents, and AgentCore
• Open Source Contributor: Active contributions to Apache Spark, Apache Kafka, Apache Burr, and Terraform ecosystems
• Stack Overflow Impact: Technical insights reaching 7.5+ million users
• GitHub Recognition: 1400+ stars across repositories and wikis
• AWS Professional Services: Architecting enterprise-grade solutions for global financial institutions
• Community Leader: 243 stars on Apache Kafka POC, 70 stars on DDD resources, 1.3k+ forks across projects

Creator of original frameworks for distributed systems and data engineering:

The Vaquar Pattern core invariant:

commit_metadata ⟹ VRP = PASS

No Iceberg snapshot, Glue catalog update, or marketplace listing may proceed unless multiset verification passes for every committed chunk.

See the full Open Source Projects & Packages section below for detailed descriptions, install commands, and download stats.

📦 Published Packages

Registry	Package	Description	Install
	sparkrules v1.2.0	Drools-equivalent business rule engine for Python — DRL syntax, decision tables, adverse-action notices, Spark integration	pip install sparkrules
	genprm	Autonomous data engineering agent — generative process supervision, MCTS inference, RL fine-tuning for SQL/ETL self-correction	pip install genprm
	iceguard v1.0.0	Reliability library for Spark-on-AWS-Lambda — timeout rollback, checkpoints, orphan cleanup. Also on Docker (GHCR)	pip install iceguard
	veridata-vrp	Offline VRP verifier — tamper-evident reconciliation proofs for data pipelines	pip install veridata-vrp
	mcp-bastion-python	Enterprise MCP security middleware + 16 framework integrations (LangChain, OpenAI, Anthropic, Bedrock…)	pip install mcp-bastion-python
	mcp-test-harness v1.1.0	Testing framework for MCP servers — functional, regression, performance. Also on Docker (GHCR)	pip install mcp-test-harness
	@mcp-bastion/core	TypeScript/Node.js MCP security middleware	npm install @mcp-bastion/core
	ai-agent-java-sdk v0.1.0	Model-driven autonomous AI agent SDK for Java — zero-trust MCP security, Spring AI AgentCore integration	io.github.vaquarkhan:ai-agent-java-sdk-core
	aiv-gate	AI-powered PR integrity gate — density, design, dependency & invariant checks	io.github.vaquarkhan:aiv-gate
	aiv-cli	CLI companion for aiv-integrity-gate	io.github.vaquarkhan:aiv-cli

The business rule engine that Python was missing. Drools-style DRL syntax, explainable decisions, regulatory-grade audit trails — from laptop to lakehouse, no JVM required.

Key Features:

• 🎯 Drools-style DRL — same syntax, no JVM, Python-native
• 📊 Decision Tables — XLSX-style with hit policies (UNIQUE, FIRST, PRIORITY, COLLECT)
• ⚖️ Regulatory Compliance — ECOA/FCRA/GDPR Art 22 adverse-action notices
• 🔍 Data Quality + Profiling — built-in DQ checks + statistical profiling
• 🌐 FastAPI + Rules Workbench — browser-based Monaco DRL editor with LSP
• 🧪 Simulation Modes — shadow, counterfactual, coverage, chain
• ⚡ Performance — ~199K evals/sec, 840+ tests, 100% line coverage
• 🚀 Multi-Platform — AWS Glue, Databricks, GCP Dataproc, Azure Synapse, Kubernetes

Enterprise-grade security middleware for the Model Context Protocol — 100% local, <5ms overhead, 16 framework integrations.

Problems Solved: Prompt injection & jailbreaks · PII leakage to LLMs · Runaway agents burning API budget · Unpredictable agentic behavior

Features: Meta PromptGuard · Microsoft Presidio PII redaction · Token budget & rate limiting · Infinite loop protection · RBAC · Schema validation · Replay guard · Cost tracker · Semantic cache · Audit logging

Framework Integrations: LangChain · OpenAI · Anthropic · Amazon Bedrock · Google Vertex AI · Cohere · Mistral · Hugging Face · LlamaIndex · CrewAI · AutoGen · Semantic Kernel · Spring AI · FastMCP · and more

Eliminates reviewer overload and low-quality PRs with automated density, design, dependency, and invariant gates.

Gates: Logic Density & Entropy · YAML Design Rules (forbidden/required patterns) · Import Validation vs pom.xml/requirements.txt · Property-based Invariant Tests · /aiv skip for urgent merges · Refactor exception · Trusted authors bypass

Production-grade skill registry for AI data engineering agents — 73 workflows, 14 platform presets, multi-agent packaging.

Lifecycle: /spec → /plan → /build → /validate → /review → /backfill → /ship

Platform Presets: AWS · Azure · GCP · Databricks · Snowflake · Alibaba Cloud · Informatica · Talend · Apache Spark · Flink · Airflow · Kafka · Iceberg

Agent Integrations: Cursor · Claude · Copilot · Kiro · Codex · OpenCode · Windsurf · AGENTS.md

Skill Coverage: Ingestion · Transformation · Orchestration · Streaming · Lakehouse · Warehousing · Governance · Quality · Modernization · Release · Incident Recovery · Platform Operations

AI agents often default to the shortest path — which is dangerous in data systems:

• ❌ Skipping specification and contract definition
• ❌ Treating a successful run as proof of correctness
• ❌ Ignoring replay, backfill, and consumer impact
• ❌ Leaving lineage, access, retention, and ownership implicit

This project enforces engineering discipline on AI agents — the same standards used by strong data engineering teams.

The included agent benchmark pack measures skill impact quantitatively:

graph LR
    A[2002: Career Start] --> B[2013: Apache Spark<br/>Contributor]
    B --> C[2015: JSR 368<br/>Expert Group]
    C --> D[2024: Published<br/>Author · Packt]
    D --> E[2025: 3 Kafka KIPs<br/>+ Spark SPIP]
    E --> F[2026: Vaquar Pattern<br/>+ InfoQ Author]
    F --> G[2026: 10 Published<br/>Packages]
    
    style A fill:#ff6b6b
    style B fill:#4ecdc4
    style C fill:#45b7d1
    style D fill:#96ceb4
    style E fill:#ffeaa7
    style F fill:#a29bfe
    style G fill:#fd79a8

My open-source repositories and technical wikis have been cited as foundational references in advanced postgraduate research across multiple continents and critical domains:

Data Engineering with AWS Cookbook (Packt, 2024) is cataloged in the library systems of the following universities, available as a resource for students and faculty in data engineering and cloud computing programs:

My wikis, repos, and contributions are cited across blogs, newsletters, and open-source communities:

Videos that cite my Stack Overflow answers (7.5M+ reach):

Find more videos: Many additional videos cite my answers across these channels. Browse or search for topics I frequently answer:

• The Debug Zone — Stack Overflow–based debugging tutorials
• Roel Van de Paar — Technical Q&A from Stack Overflow/ServerFault (2M+ videos)
• Search: vaquarkhan stackoverflow

Topics I often answer: Apache Spark, Kafka, AWS (Elastic Beanstalk, RDS, API Gateway), Spring Boot, Docker, Maven/Jacoco

📖 Published Works

Data Engineering AWS Cookbook Recipe-based guide for AWS data engineering

Microservices Recipes A comprehensive free GitBook on microservices patterns ⭐ Free & Open Source ⭐ 600+ GitHub Stars · 280+ forks

• 🔥 Apache Spark Community Contributions
• 📋 JCP Member - JSR-368

🎯 Book a 1:1 Mentorship Session

I offer personalized mentorship in cloud architecture, microservices, data engineering, and career guidance for aspiring architects and senior engineers.

Topics I Can Help With:

• ☁️ Cloud Architecture & AWS Solutions
• �-️ Microservices Design & Implementation
• 📊 Big Data Engineering & Analytics
• 🎯 Career Progression to Senior/Principal/Architect Roles
• 🔧 System Design & Distributed Systems
• 💡 Technical Leadership & Team Management

🏅 GitRanks — Global & USA Rankings

Metric	Global Rank	USA Rank
Overall	Elite 5	Legend 1
Stars (2,593 total)	Elite 4 — Top 2% (#14,754 of 834K)	Elite 4 — Top 2% (#2,279 of 138.6K)
Followers (704 total)	Elite 5 — Top 2% (#12,333 of 1.2M)	Legend 1 — Top 1% (#2,228 of 254K)

📊 Profile Summary

🌐 Stack Overflow

�-�️ Isometric Contribution Calendar

📈 Contribution Graph

🐍 Contribution Snake

💡 If the snake animation is not visible, run the GitHub Action once to generate it.

🌍 Empowering Global Innovation Through Open Source

💼 Open to Collaboration | 🎯 Available for Mentorship | 📚 Sharing Knowledge

Empowering researchers, engineers, and architects worldwide 🚀

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_{⚡ 10 packages published · 3 KIPs authored · 73 agent skills · 12 years in Apache open source · Cited in Q1 journals}