ddbMigrator: High-Performance DynamoDB Migration Tool in Rust and Python

Handling 10,000+ market ticks per second using Python without blocking the event loop is a pure system design challenge. 📈 Over my time architecting automated trading pipelines, I’ve learned that a naive while True loop will crash your system the moment market volatility spikes. You need a decoupled, event-driven architecture. Here is a blueprint for a scalable data ingestion engine: ⚙️ 1. Asynchronous I/O is Non-Negotiable Using asyncio (or asynchronous C++ for hardcore HFT) is mandatory for WebSocket connections. The ingestion worker should do ONE thing: read the socket and push to a queue. Absolutely no parsing or mathematical calculations on this thread. ⚡ 2. The L1 "Hot" Cache Push the incoming tick data directly into an in-memory datastore like Redis (using Redis TimeSeries). This allows your strategy containers to instantly fetch rolling windows (e.g., the last 50 ticks for a Z-score calculation) in sub-millisecond time. 🗄️ 3. The L2 "Cold" Storage Simultaneously, a background worker should batch these ticks and asynchronously write them to a time-series database like InfluxDB. This keeps your hot cache clean and provides a robust historical dataset for backtesting new quantitative models later. 🛠️ 4. Containerized Isolation Run the ingestion engine, the strategy engine, and the execution engine in separate Docker containers. If the execution engine crashes due to an API limit, your ingestion engine shouldn't stop recording market data. By decoupling ingestion from execution, you build a resilient system that thrives during extreme market volatility. How do you handle real-time data bursts in your architectures? #SystemArchitecture #DataEngineering #Python #Asyncio #Microservices #Docker #QuantFinance #TechCareers

What happens between your API returning 202 and the customer receiving a confirmation email? Day 17 of 30 -- Background Tasks with Celery Phase 3 -- Backend and APIs If the answer is "the route handler does it all" -- your architecture has a problem. The correct answer: Celery. Your API queues a task and returns 12ms. A separate worker process picks up the task from Redis, executes it, retries on failure, stores the result, and notifies when done. The API and the worker never share a process. That is the entire point. Today's Topic covers everything: Why background tasks exist and the problem they solve Full 5-component architecture diagram 6 task state machine -- PENDING through FAILURE Annotated syntax -- setup, bind, retries, rate limits, beat schedule chain, group, chord -- sequential, parallel, parallel-with-callback Real order pipeline -- 6 tasks in 1.8s, API responded in 12ms Queue routing -- critical queue for payments, low queue for emails Celery Beat -- daily reports, hourly sync, weekly cleanup in pure Python 5 mistakes including the pickle vulnerability and the double Beat trap Question for you: What was the longest task you ever ran synchronously inside an API handler? Drop it below. #Python #Celery #BackgroundTasks #FastAPI #Redis #100DaysOfCode #BackendDevelopment #TechContent #BuildInPublic #TechIndia #DistributedSystems #LinkedInCreator #LearnPython #OpenToWork #SoftwareEngineering #PythonTutorial

I've been working through how MCP and A2A fit together in practice, specifically how to keep agent context clean as you scale across multiple service domains. Wrote up what I learned: how progressive reveal lets an MCP server guide agent reasoning through layers of capability discovery, and why pairing that with A2A object handoffs solves the context churn problem that shows up when agents navigate deep tool hierarchies. There's a working Python MCP server you can clone and run, plus research citations on why context management matters more than most people think. #MCP #A2A #AIAgents #SoftwareArchitecture #Python

Most RAG implementations reach for a vector database before asking whether they actually need one. For structured data, high-churn content, or anything that needs explainable retrieval — they often don't. Vectorless RAG uses what you already have: Elasticsearch, Postgres, or any full-text search backend. No embeddings. No sync pipelines. No new services. Here's the full breakdown — concept, architecture, tradeoffs, and a quick-start in Python. #RAG #LLM #AIEngineering #GenerativeAI

𝗣𝘆𝘁𝗵𝗼𝗻 + 𝗞𝗮𝗳𝗸𝗮 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲: 𝗗𝗲𝘀𝗶𝗴𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗳𝗼𝗿 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Building real-time systems that handle millions of events isn’t just about plugging Python into Kafka. It’s about designing systems that don’t break under pressure. I came across this solid breakdown of Kafka design patterns and it reinforces one thing 👇 👉 Architecture matters more than code when you scale. 🔗 Read the full article https://lnkd.in/gVUz6rT5

Just implemented a Celery-based background processing layer for a Django real-time messaging system. This layer handles critical async operations that shouldn’t block user requests: • Syncing unread message counters from Redis → PostgreSQL • Ensuring message state durability beyond in-memory cache • Retry-safe background execution with Celery task retries • Foundation for offline message delivery (push notifications) • Cleanup hooks for stale real-time states like “typing…” indicators The key design idea: Redis handles speed, Celery ensures reliability, and the database remains the source of truth. This separation is what makes real-time systems scalable without losing consistency under load. #Django #Celery #BackendEngineering #SystemDesign #Python #SoftwareEngineering #Scalability

Excited to share that I’ve contributed to the latest release of AWS Lambda Powertools for Python (v3.26.0) https://lnkd.in/dG3C_R9P 🚀 This release introduces useful enhancements, including a new Lambda Metadata utility and improvements across logging, batch processing, and more. I’m particularly happy to have contributed to the logging space, helping improve how external loggers can be integrated with Powertools through buffering support - making observability in Lambda even more robust and flexible. Big thanks to the maintainers and contributors for the collaboration 🙌 If you’re building serverless applications on AWS, Powertools is definitely worth checking out. #AWS #Serverless #Python #OpenSource #CloudComputing #AWSLambda

Migrate multiple S3 buckets in parallel: one YAML config per bucket pair, a Python script that fans out N godwit sync processes, and a single Grafana dashboard showing all runs. Each process gets its own state DB, metrics port, and resume point. Failures are per-pair, retry resumes at the object level. Guide: https://lnkd.in/evvaGAdK Lab: https://lnkd.in/eadBtNCn #S3 #DataMigration #DevOps #Prometheus #Grafana #Observability

Microsoft Agent Framework (MAF) v1.0 has been released. This is the production-ready release: stable APIs, and a commitment to long-term support. Whether you’re building a single assistant or orchestrating a fleet of specialized agents, Agent Framework 1.0 gives you enterprise-grade multi-agent orchestration, multi-provider model support, and cross-runtime interoperability via A2A and MCP. https://lnkd.in/e7VP2eWG #MAF #AI #AIAgents #Python #DoNet

Real-time? Go with WebSockets for persistent, bidirectional communication. Need to scale? Deploy Kafka to handle massive message throughput. Keeping it simple? Stick to REST for straightforward, universal APIs. Embracing the chaos? Use GraphQL to let clients query exactly what they need. Building AI? It’s Python, thanks to its unbeatable ecosystem. Coding infra? Go is the gold standard for cloud-native tooling. Hunting logs? ElasticSearch is the go-to for deep search and indexing. Cutting latency? Redis is king for lightning-fast, in-memory data. High availability? Trust Postgres for rock-solid relational data. Processing streams? Lean on Flink for real-time data pipelines. Staying low-level? Classic C for direct hardware control. Max performance? Power through heavy workloads with C++. Going Enterprise? Java remains the backbone of large-scale corporate systems. #WebSockets #Kafka #REST #GraphQL #Techstack