Also known as: Vaquar Khan | Viquar Khan
Vaiquar 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.
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β ποΈ Cloud Architecture π Big Data Engineering β
β π€ GenAI & Agentic AI π§ Microservices Design β
β π° Financial Services π― Domain-Driven Design β
β π Technical Leadership π Open Source Contribution β
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- JSR 368 Expert Group Member: Shaped industry standards for Javaβ’ Message Service 2.1
- 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 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
| Project | Proposal | Description |
|---|---|---|
| Apache Kafka | KIP-1267: Tiered Storage Cost Attribution Metrics | Client-level cost attribution for Kafka Tiered Storage β enables FinOps, chargeback, and rogue consumer detection in multi-tenant clusters |
| Apache Kafka | KIP-1316: Circuit Breaker for Share Group DLQ Overflow | Prevents cascading failures when Share Group DLQ fills up β introduces circuit breaker pattern to protect cluster stability at scale |
| Apache Kafka | KIP-1317: Mandatory DLQ Disposition Header for Share Groups | Ensures every DLQ-routed record carries authoritative disposition metadata β enables observability, audit, and automated remediation |
| Apache Spark | SPIP: Asynchronous Metadata Resolution & Lazy Prefetching for Spark Connect | Performance optimization for Spark Connect metadata resolution and prefetching |
| Apache Iceberg | Real-Time Agentic RAG Architecture with Iceberg v3 | Published architecture leveraging Iceberg v3 deletion vectors + Spark 4.1 Intent-Driven Design for low-latency CDC in agentic AI systems |
| Project | Issue | Description |
|---|---|---|
| Terraform AWS Provider | #38744: glue_data_quality_ruleset rules not supporting multi line string | Bug report & resolution β AWS Glue Data Quality ruleset failed with heredoc multiline strings; documented workaround using join() for readable DQDL rules |
| Terraform AWS Provider | #39821: aws_glue_security_configuration should support encrypting Glue Data Quality | Enhancement request β Add data_quality_encryption block to fix security findings when S3/KMS/CloudWatch are encrypted but Glue Data Quality remains unencrypted |
Creator of groundbreaking frameworks for distributed systems:
- The Khan Pattern for Adaptive Granularity
- The Khan Granularity Protocolβ’
- The Khan Microservices Maturity Model (KM3β’)
Original syntheses and scoring methodologies designed to operationalize distributed systems theory
See the full Open Source Projects & Packages section below for detailed descriptions, install commands, and download stats.
The business rule engine that Python was missing. Drools-style DRL syntax, explainable decisions, regulatory-grade audit trails β from laptop to lakehouse, no JVM required.
Key Features:
- π― Drools-style DRL β same syntax, no JVM, Python-native
- π Decision Tables β XLSX-style with hit policies (UNIQUE, FIRST, PRIORITY, COLLECT)
- βοΈ Regulatory Compliance β ECOA/FCRA/GDPR Art 22 adverse-action notices
- π Data Quality + Profiling β built-in DQ checks + statistical profiling
- π FastAPI + Rules Workbench β browser-based Monaco DRL editor with LSP
- π§ͺ Simulation Modes β shadow, counterfactual, coverage, chain
- β‘ Performance β ~199K evals/sec, 840+ tests, 100% line coverage
- π Multi-Platform β AWS Glue, Databricks, GCP Dataproc, Azure Synapse, Kubernetes
π Use Cases
| Domain | Scenario |
|---|---|
| π³ Lending | Loan underwriting + adverse-action notices for declines |
| π° Payments | POS end-of-day batch rule evaluation |
| π₯ Healthcare | Clinical trial eligibility screening |
| π‘οΈ Fraud | Real-time transaction authorization with explainable declines |
| π Compliance | Deterministic settlement replay for audit |
| π¦ Insurance | Claims adjudication via decision tables |
Prompt injection defense, PII redaction, rate limiting, and circuit breaker for Model Context Protocol β 100% local, <5ms overhead.
Problems Solved: Prompt injection & jailbreaks Β· PII leakage to LLMs Β· Runaway agents burning API budget Β· Unpredictable agentic behavior
Features: Meta PromptGuard Β· Microsoft Presidio PII redaction Β· Token budget & rate limiting Β· Infinite loop protection Β· RBAC Β· Schema validation Β· Replay guard Β· Cost tracker Β· Semantic cache Β· Audit logging
Eliminates reviewer overload and low-quality PRs with automated density, design, dependency, and invariant gates.
Gates: Logic Density & Entropy Β· YAML Design Rules (forbidden/required patterns) Β· Import Validation vs pom.xml/requirements.txt Β· Property-based Invariant Tests Β· /aiv skip for urgent merges Β· Refactor exception Β· Trusted authors bypass
| Repository | Lang | β | Description |
|---|---|---|---|
| vaquarkhan/vaquarkhan | Wiki | 1.5K+ | Technical wiki β Spark, Kafka, Microservices, DDD, Cloud Architecture |
| data-engineering-agent-skills | β | β | Data Engineering Agent Skills β reusable AI agent capabilities for data pipelines |
| IceGuard | Python | 1 | Reliability library for Spark-on-AWS-Lambda writes β timeout-aware rollback, resumable checkpointing, orphan cleanup, multi-Lambda coordination, CloudWatch observability |
| ai-agent-java-sdk | Java | 2 | Java SDK for building AI agents β lightweight, extensible agent framework |
| mcp-test-harness | Python | 2 | Testing framework for MCP servers β validate tool schemas, test prompts, assert responses |
| spring-ai-agentcore | Java | 1 | Fork of spring-ai-community/spring-ai-agentcore β Spring Boot integrations for Amazon Bedrock AgentCore |
| spring-ai-agentcore-observability | Java | β | Observability extensions for Spring AI AgentCore β tracing, metrics, and monitoring |
| burr | Python | 1 | Fork of apache/burr β Build applications that make decisions (chatbots, agents, simulations). Monitor, trace, persist, and execute on your own infrastructure |
| microservices-recipes-a-free-gitbook | GitBook | 600+ | Free GitBook on microservices patterns (280+ forks) |
| Apache-Kafka-poc-and-notes | Java | 243+ | Apache Kafka POC with comprehensive notes & patterns |
| apache-kafka-spark-streaming-poc | Java | 11 | Kafka + Spark Streaming integration POC (15 forks) |
| awesome-spring-reactive-webflux | Java | 4 | Spring Reactive WebFlux β Mono/Flux diagrams (13 forks) |
| Real-time-Fraud-Analysis-Spark | Scala | β | Real-time fraud detection with Kafka, Spark & Cassandra |
Active contributor to the Spring AI ecosystem and Amazon Bedrock AgentCore β bridging enterprise Java with next-gen agentic AI:
| Project | Contribution | Description |
|---|---|---|
| Spring AI Community | spring-ai-agentcore | Spring Boot starter enabling existing apps to conform to AWS Bedrock AgentCore Runtime contract with minimal configuration |
| Spring AI AgentCore | spring-ai-agentcore-observability | Observability extensions β tracing, metrics, and production monitoring for AgentCore-powered agents |
| Amazon Bedrock AgentCore | agentcore-samples | Contributing production-ready samples for deploying AI agents with enterprise-grade scale, reliability, and security |
| Spring AI + Bedrock | HackerNoon: Production Observability for Spring AI Agents | Published architecture for zero-code observability of Spring AI agents on Amazon Bedrock |
| Project | Contribution | Description |
|---|---|---|
| Apache Burr | Fork & Contributions | Contributing to the open-source framework for building stateful AI agent applications β chatbots, agents, simulations with monitoring, tracing, and persistence |
graph LR
A[22+ Years Experience] --> B[JSR 368 Expert Group]
B --> C[AWS Professional Services]
C --> D[Published Author]
D --> E[7.5M+ SO Impact]
E --> F[Academic Citations]
F --> G[The Khan Patternβ’]
style A fill:#ff6b6b
style B fill:#4ecdc4
style C fill:#45b7d1
style D fill:#96ceb4
style E fill:#ffeaa7
style F fill:#dfe6e9
style G fill:#a29bfe
My open-source repositories and technical wikis have been cited as foundational references in advanced postgraduate research across multiple continents and critical domains:
| Institution | Country | Research Domain | Citation Impact | PDF Β· Research |
|---|---|---|---|---|
| IEEE ICCCBDA 2025 | π International | Supply Chain Data Management | Data Engineering with AWS Cookbook cited as reference for AWS-based ETL architecture | IEEE Xplore |
| University of Southern Denmark | π©π° Denmark | Intelligent Transportation Systems (V2X) | Smart City traffic management & GLOSA systems | π Thesis PDF |
| University of Toronto | π¨π¦ Canada | Healthcare Big Data Analytics | MRI wait-time optimization (600GB dataset) | π Thesis PDF |
| National Technical University of Athens | π¬π· Greece | Cloud Computing & Kubernetes | Novel autoscaling algorithms for local storage | π Thesis PDF |
| Multi-National Collaboration | π Global | Blockchain Scalability | Published in Future Generation Computer Systems (Q1 Journal) | π Survey PDF Β· ScienceDirect Β· ACM |
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:
| University | Country | Library System |
|---|---|---|
| Brandeis University | πΊπΈ USA | Brandeis OneSearch β available for M.S. Strategic Analytics & Computer Science programs |
| Princeton University | πΊπΈ USA | Princeton University Library β science & engineering collections |
| Northumbria University | π¬π§ UK | Northumbria University Library Search |
My wikis, repos, and contributions are cited across blogs, newsletters, and open-source communities:
Videos that cite my Stack Overflow answers (7.5M+ reach):
| Video | Channel | Link |
|---|---|---|
| Why is my Spark job getting stuck when collect() is called? | vlogize | Watch |
| How to associate an existing RDS instance to an Elastic Beanstalk environment? | Roel Van de Paar | Watch |
Find more videos: Many additional videos cite my answers across these channels. Browse or search for topics I frequently answer:
- The Debug Zone β Stack Overflowβbased debugging tutorials
- Roel Van de Paar β 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
| Source | What's Cited | Link |
|---|---|---|
| Get Kafka-Nated (Substack) | Kafka mailing list thread on cloud-native KIPs; KIP-1267 (Tiered Storage Cost Attribution) | Biweekly #276 |
| Gradle Discuss | Microservice example from GitHub (troubleshooting run) | Thread #43549 |
| Dev.to | CQRS & Event Sourcing wiki | Deep Dive into Microservices |
| Medium (Jon SY Chan) | Horizontal vs Vertical scaling wiki | Scaling up Concepts for Servers |
| Medium (Shiksha Engineering) | awesome-spring-reactive-webflux (Reactor Mono/Flux diagrams) | Reactive Programming |
| Apache Spark User List | Codegen 64KB limit; Kafka vs Spark Streaming (community help) | msg69132 Β· msg62385 |
| Oracle JMS 2.1 | JMS Expert Group participation (meeting minutes) | Meeting 3 Β· Meeting 2 Β· Sep |
| DZone | 3 articles, 118K+ pageviews | Profile |
| Eclipse Jersey | Bug report β HashMap JSON serialization | #3432 |
| Apache Amoro | Technical analysis β reachMinorInterval "noisy neighbor" fix | #4055 |
| Jakarta Messaging | JMS INDIVIDUAL_ACKNOWLEDGE spec discussion | #95 |
| data-dot-all | Bug report β Windows CDK deployment (workaround: WSL) | #340 |
| AWS Athena Query Federation | Feature request β DynamoDB table filter for Athena (PR #607) | #606 |
| Domain | Impact | Scale |
|---|---|---|
| π Smart Cities | Backend architecture for V2X traffic management | Reducing carbon emissions across European cities |
| π₯ Healthcare | Big data pipelines for medical imaging analytics | Processing 600GB+ datasets for cancer diagnosis optimization |
| βοΈ Cloud Infrastructure | Kubernetes autoscaling innovations | Enabling cost-efficient resource utilization at scale |
| βοΈ Blockchain | Knowledge curation & scalability research | Supporting systematic reviews in Q1 journals |
| π° Financial Services | AWS data solutions for global institutions | Empowering fintech transformation at enterprise scale |
| π Education | Open-source technical resources | Cited by researchers at top universities worldwide |
| Article | Platform | Topic |
|---|---|---|
| Deploying AWS Glue Data Quality Pipelines Using Terraform | AWS Big Data Blog | IaC best practices for Glue Data Quality β consistent, version-controlled deployments across environments |
| Article | Published | Topic |
|---|---|---|
| Production Observability for Spring AI Agents on Amazon Bedrock Without Writing Tracing Code | May 2026 | Zero-code observability for Spring AI agents on Bedrock β OpenTelemetry, X-Ray, and CloudWatch integration |
| Real-Time Agentic RAG: Eradicating Context Rot With Spark & Iceberg | Mar 2026 | Architecture using Spark 4.1 & Apache Iceberg v3 deletion vectors for low-latency CDC to keep embedding stores fresh |
| Article | Views | Topic |
|---|---|---|
| AWS Lambda With MySQL (RDS) and API Gateway | 47K+ | Microservices with AWS API Gateway & RDS |
| Run AWS Lambda Functions Locally on Windows | 60K+ | SAM Local for Lambda development |
| Fast Data Access: GemFire + Apache Spark | 12K+ | In-memory data grid with Spark |
| Article | Topic |
|---|---|
| Amazon API Gateway with Spring Boot β Tricks and Hacks | REST, WebSocket, HTTP API patterns with Spring Boot on AWS |
| Article | Published | Topic |
|---|---|---|
| Architecting Cloud-Native Kafka: From Tiered Storage Towards a Diskless Future | 2026 | Deep-dive into Kafka's cloud-native evolution β Tiered Storage economics, KIP-1267 cost attribution, KIP-848 consumer rebalancing, KIP-932 Share Groups, KIP-1134 Virtual Clusters, and the diskless future (KIP-1150/1163). References KIP-1316 & KIP-1317. |
| Source | Coverage | Link |
|---|---|---|
| InfoQ (Article) | "Architecting Cloud-Native Kafka" β flagship article covering Tiered Storage, FinOps, Share Groups, Virtual Clusters, and the Diskless future. Directly references KIP-1267, KIP-1316, KIP-1317 | Read |
| LetsDataScience | "Viquar Khan Proposes Real-Time RAG Architecture" β featured news coverage of the Spark + Iceberg agentic RAG approach | Read |
| Get Kafka-Nated (Substack) | KIP-1267 featured in Biweekly #276 β cloud-native Kafka KIPs newsletter | Read |
| HackerNoon TechBeat | Featured in "The TechBeat" newsletter (Apr 4, 2026) β deep dive into AI Context Rot | Read |
| Business Intelligence Group | Judge / Evaluator | Profile |
I offer personalized mentorship in cloud architecture, microservices, data engineering, and career guidance for aspiring architects and senior engineers.
Topics I Can Help With:
- βοΈ Cloud Architecture & AWS Solutions
- ποΈ Microservices Design & Implementation
- π Big Data Engineering & Analytics
- π― Career Progression to Senior/Principal/Architect Roles
- π§ System Design & Distributed Systems
- π‘ Technical Leadership & Team Management
| Metric | Global Rank | USA Rank |
|---|---|---|
| Overall | Elite 5 | Legend 1 |
| Stars (2,593 total) | Elite 4 β Top 2% (#14,754 of 834K) | Elite 4 β Top 2% (#2,279 of 138.6K) |
| Followers (704 total) | Elite 5 β Top 2% (#12,333 of 1.2M) | Legend 1 β Top 1% (#2,228 of 254K) |




