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Collection of SQL patterns, query optimization techniques, and relational schema designs. A comprehensive reference library for database engineering, focus on data integrity, and performant query execution.
Comprehensive study of relational database systems (RDBMS). Focused on schema design, query optimization, data normalization, and integrity constraints for scalable backend architectures.
Enterprise-grade database management capstone project focusing on normalized schema architecture, complex query optimization, and transaction integrity. An end-to-end RDBMS solution for scalable business data modeling.
Comprehensive SQL data management practices. Covering relational database design, query optimization, data integrity, and scalable schema architecture. A foundational library for backend development.
J4All Core Platform served as foundational infrastructure for broader application and workflow orchestration across the J4All ecosystem. The project focused on reusable backend architecture, shared platform services, API orchestration, and scalable integration patterns.
Production-style distributed workflow orchestrator in Go — DAG scheduling, worker leases, heartbeats, exponential backoff retry, dead letter queue, idempotency, cron scheduling, Prometheus metrics
Production-style AI backend platform built with FastAPI, PostgreSQL, RAG, and MCP. Demonstrates real-world AI platform engineering: typed APIs, RBAC, vector search, dockerized infra, and full pytest coverage.
A backend operating environment that orchestrates plugin processes, provides HTTP/gRPC gateway services, and handles infrastructure concerns such as auth, routing, rate limiting, lifecycle management, and observability.
PIE pre-computes derived state at write-time via causal dependency graph traversal, reducing read-path latency to O(1) regardless of underlying computational complexity.
Core backend infrastructure for a conceptual lending platform. Built with Django and PostgreSQL, focusing on ACID-compliant financial data modeling, secure REST APIs, and strict data integrity.
A custom distributed training framework implementing ZeRO-1 and ZeRO-2 memory partitioning from scratch in PyTorch. Features async gradient hooks, sharded AdamW, and distributed checkpointing without relying on FSDP or HuggingFace Trainer.