I'm Sunil Kumar Reddy, a Data Engineer passionate about designing and building production-grade, cloud-native data platforms capable of processing large-scale streaming workloads.
My primary interests include:
- ⚡ Real-Time Streaming Pipelines
- 🏗️ Distributed Data Systems
- ☁️ Cloud-Native Data Engineering
- 📊 ETL & ELT Platform Development
- 📈 Observability & Monitoring
- 🌍 Open Source Development
I enjoy building reliable, scalable, and maintainable data platforms using modern distributed technologies while continuously improving my engineering skills through open-source contributions.
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Production-grade streaming platform for real-time fraud detection using stateful stream processing, exactly-once semantics, and cloud-native architecture.
- ⚡ Stateful stream processing with Apache Flink
- 🚀 Low-latency fraud detection
- 🔄 Exactly-once event processing
- 📈 Horizontal scalability
- 📊 Live operational dashboards
- 📡 Event-driven architecture
- ☁️ Cloud-native deployment
- 🔍 End-to-end observability
| Metric | Value |
|---|---|
| Average Volume | 100M+ Transactions/Day |
| Daily Data | 1–2 TB |
| Processing | Real-Time Streaming |
| Processing Guarantee | Exactly Once |
📂 Repository: https://github.com/sunildataengineer/Real-Time-Fraud-Detection-Risk-Intelligence-Platform
🖥️ Architecture: 
🖥️ Data Modelling: 
🌐 Live Demo: Coming Soon
Production-grade data quality platform that validates, governs, monitors, and scores streaming data before downstream consumption.
- ✅ Schema validation
- 📋 Data quality scoring
- 🔍 Data governance
- 📈 Live monitoring
⚠️ Automatic anomaly detection- 🔄 Dead Letter Queue handling
- 📊 Data quality dashboards
- ☁️ Cloud-native deployment
| Metric | Value |
|---|---|
| Average Volume | 50M+ Records/Day |
| Daily Data | 500 GB–1 TB |
| Processing | Real-Time Streaming |
| Focus | Data Quality & Governance |
📂 Repository: Add GitHub Repository Link
🖥️ Architecture: Add Architecture Diagram
🌐 Live Demo: Optional
Scalable event processing platform built for high-throughput, fault-tolerant, low-latency analytics with stateful stream processing.
- 🌍 Event-driven architecture
- ⚡ Stateful processing
- 🪟 Event-time windowing
- 🔁 Checkpoint & recovery
- 📈 Horizontal scaling
- 📊 Live dashboards
- ☁️ Cloud-native deployment
- 📡 High availability
📂 Repository: Add GitHub Repository Link
🖥️ Architecture: Add Architecture Diagram
🌐 Live Demo: Optional
Build reliable, observable, scalable, and production-ready data platforms that transform high-volume event streams into trusted, actionable data through modern cloud-native engineering practices.
Contributing to one of the world's most widely adopted workflow orchestration platforms used by thousands of organizations for production data engineering.
Project
Apache Airflow
Area
SFTP Provider
Feature
Deferrable SFTPOperator
Pull Request
PR #68298 (continuation of the original implementation)
Status
Active review
The existing SFTPOperator occupied an Airflow worker for the entire duration of long-running file transfers.
This reduced worker availability and limited scalability for workflows involving large or slow SFTP operations.
The goal of this contribution is to introduce a deferrable execution mode, allowing the operator to release the worker after initiating the transfer and resume execution only when the asynchronous operation completes.
- Implemented asynchronous execution using
self.defer() - Integrated with Airflow's Triggerer architecture
- Enabled non-blocking execution for long-running SFTP transfers
Designed and implemented
SFTPOperationTrigger
Responsibilities include:
- Waiting asynchronously for transfer completion
- Returning execution events
- Reducing worker utilization
- Supporting scalable task execution
Refactored transfer logic into shared methods across synchronous and asynchronous hooks to eliminate duplicated code and improve maintainability.
Applied the DRY (Don't Repeat Yourself) principle based on maintainer feedback.
Replaced wrapper-based asynchronous execution with native async operations for improved efficiency.
Implemented asynchronous file operations including:
- retrieve
- store
- delete
Implemented bounded concurrent transfers using:
asyncio.Semaphoreasyncio.gather
Benefits:
- Controlled concurrency
- Reduced connection overhead
- Improved throughput
- Better resource utilization
Resolved multiple review iterations including:
- Ruff linting
- Import ordering
- Documentation updates
- News fragments
- Exception handling
- CI failures
- Formatting improvements
Worked with Apache Airflow maintainers and contributors through multiple review cycles, incorporating feedback on:
- Architecture
- Naming conventions
- API design
- Performance
- Maintainability
- Code quality
This iterative review process strengthened both the implementation and my understanding of large-scale open-source collaboration.
- Asynchronous Programming
- Apache Airflow Internals
- Python
- Open Source Collaboration
- Distributed Systems
- Code Review
- Performance Optimization
- Git Workflow
- CI/CD Debugging
- Software Design Principles
Contributing to Apache Airflow provided hands-on experience with:
- Designing production-quality features
- Working within a large, mature codebase
- Responding to maintainer feedback
- Iterating through multiple review cycles
- Maintaining backward compatibility
- Writing clean, maintainable, and testable code
This experience reinforced the importance of thoughtful design, collaboration, and continuous improvement in production software engineering.
- Apache Airflow: https://github.com/apache/airflow
- Pull Request: apache/airflow#68298
Contributing to Apache Airflow — one of the world's most widely adopted workflow orchestration platforms.
Apache Airflow is one of the most popular workflow orchestration platforms used by organizations worldwide for scheduling, orchestrating, and monitoring complex data pipelines.
As an open-source contributor, I worked on improving the SFTP Provider by implementing Deferrable Execution, enabling long-running SFTP transfers to execute asynchronously without occupying Airflow worker resources.
Traditional SFTPOperator execution keeps an Airflow worker occupied during the entire file transfer.
Worker
↓
Transfer Running
↓
Worker Busy
↓
Transfer Finished
Problems
• Worker Slot Blocked
• Poor Resource Utilization
• Limited Parallelism
• Higher Infrastructure Cost
Introduce
Deferrable SFTPOperator
using
Airflow Triggerer
Worker
↓
Start Transfer
↓
self.defer()
↓
Triggerer
↓
Async Trigger
↓
Transfer Complete
↓
Resume Worker
↓
Task Success
This enables
• Non-blocking execution
• Better scalability
• Lower worker utilization
• Higher concurrency
User DAG
↓
SFTPOperator
↓
self.defer()
↓
Triggerer
↓
SFTPOperationTrigger
↓
Async SFTP Hook
↓
Remote SFTP Server
↓
Trigger Event
↓
Worker Resumes
↓
Task Complete
Implemented
SFTPOperationTrigger
Responsibilities
- Wait asynchronously
- Monitor file transfer
- Return completion event
Integrated
self.defer()
inside
SFTPOperator.execute()
to support asynchronous execution.
Refactored transfer implementation into
SFTPHook.transfer()
SFTPHookAsync.transfer()
Benefits
-
DRY
-
Maintainability
-
Easier testing
Removed
sync_to_async
Replaced with
retrieve_file()
store_file()
unlink()
using
native asyncio.
Implemented
asyncio.Semaphore
asyncio.gather
Benefits
-
Controlled concurrency
-
Better throughput
-
Reduced connection overhead
During the review process I addressed feedback related to
-
API Design
-
Naming
-
Performance
-
Code Reuse
-
Documentation
-
CI
-
Linting
-
Provider Standards
Worked through
-
Ruff
-
Pytest
-
Provider Tests
-
Documentation Validation
-
News Fragment Validation
-
Import Ordering
-
Formatting
-
Exception Handling
-
Distributed Systems
-
Async Programming
-
Python
-
Apache Airflow Internals
-
Git
-
GitHub
-
Code Review
-
Open Source Collaboration
-
CI/CD
-
Software Architecture
-
Performance Optimization
This contribution strengthened my understanding of
-
Production software development
-
Large-scale codebases
-
API design
-
Reviewer collaboration
-
Backward compatibility
-
Maintainable architecture
-
Async system design
-
Performance engineering
April 2025
↓
Initial Proposal
↓
Feature Development
↓
Code Review
↓
Refactoring
↓
Native Async Migration
↓
Performance Improvements
↓
Multiple Review Iterations
↓
Active Review
Python
Apache Airflow
AsyncIO
Git
GitHub
Ruff
Pytest
Open Source
Distributed Systems
CI/CD
Apache Airflow
https://github.com/apache/airflow
Pull Request
Apache Airflow Documentation