You will get KafkaDD | Deterministic Kafka Ingestion, DLQ & Replay (Java / Quarkus)
Project details
OPEN-SOURCE BACKED: Docker images + Maven Central artifacts (release live)
I deliver a production-ready Kafka consumer service (Quarkus-based) with deterministic, idempotent processing and a two-rail DLQ for production operators and platform teams.
• Pipeline DLQ (technical): serialization, schema, and infrastructure failures with partition-preserving routing and safe auto-redrive.
• Business DLQ (semantic): validation and policy rejects with a clear fix -> resend workflow (no side-effect duplication).
Operator-facing component with clean YAML configuration, strict property binding, offset/commit discipline, and a README + runbook to run, verify, observe, and replay. Replays are safe via a stable event_id; consumers dedupe before side effects.
Optional add-ons: metrics & dashboards, Kafka transactions/outbox, Spring Boot port (if required), and cloud deployment wiring.
Catalog: BraineousAI open-source reference implementations.
Reference: GitHub project “braineous-ai-platform / dd-kafka”.
LLMDD (in progress): deterministic, policy-gated LLM workflows.
Reference: GitHub project “braineous-ai-platform / dd-llm”.
I deliver a production-ready Kafka consumer service (Quarkus-based) with deterministic, idempotent processing and a two-rail DLQ for production operators and platform teams.
• Pipeline DLQ (technical): serialization, schema, and infrastructure failures with partition-preserving routing and safe auto-redrive.
• Business DLQ (semantic): validation and policy rejects with a clear fix -> resend workflow (no side-effect duplication).
Operator-facing component with clean YAML configuration, strict property binding, offset/commit discipline, and a README + runbook to run, verify, observe, and replay. Replays are safe via a stable event_id; consumers dedupe before side effects.
Optional add-ons: metrics & dashboards, Kafka transactions/outbox, Spring Boot port (if required), and cloud deployment wiring.
Catalog: BraineousAI open-source reference implementations.
Reference: GitHub project “braineous-ai-platform / dd-kafka”.
LLMDD (in progress): deterministic, policy-gated LLM workflows.
Reference: GitHub project “braineous-ai-platform / dd-llm”.
Programming Languages
JavaCoding Expertise
Performance Optimization, SecurityWhat's included
| Service Tiers |
Starter
$1,950
|
Standard
$5,950
|
Advanced
$9,500
|
|---|---|---|---|
| Delivery Time | 4 days | 8 days | 12 days |
Number of Revisions | 1 | 2 | 3 |
Number of Pages | 2 | 5 | 8 |
Design Customization | - | - | - |
Content Upload | - | - | - |
Responsive Design | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$750 - $2,500
Cloud Deploy (AWS/EKS/ECS)
(+ 3 Days)
+$1,500Frequently asked questions
About Sohil
Enterprise AI Workflow Engineer | Governance & Deterministic Systems
Austin, United States - 11:39 pm local time
My focus is building AI-backed decision flows that integrate cleanly into existing systems — without introducing black-box risk.
With deep experience in distributed systems and event-driven architecture, I design workflows that are structured, traceable, and enterprise-ready.
From ingestion through policy gates to final commit, every step is observable and production-safe.
If you're building AI systems that must meet enterprise standards — not just demos — I can help.
Steps for completing your project
After purchasing the project, send requirements so Sohil can start the project.
Delivery time starts when Sohil receives requirements from you.
Sohil works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff & scope lock
Confirm the use case, success criteria, and current failure pain (duplicates, DLQ noise, replay risk). Lock the event shape (headers incl. event_id), Kafka topic/partition expectations, and any validation rules that determine DLQ-D vs DLQ-S.
Design & contract
Produce AsyncAPI (Kafka events) + OpenAPI (admin/replay endpoints, if any) and a 1-page architecture sketch: event_id idempotency, offset/commit strategy, DLQ-S vs DLQ-D routing, replay bounds. Client review/approve before build.