EstateFlow AI Python Node.js PostgreSQL Milestone Achieved

I've been building a vector database from scratch in Python. Not a wrapper. Not a tutorial follow along, the actual thing From storage, indexing, and durability layers, all written by hand. It's called VectKV. Here's what's in it so far: • HNSW indexing (same algorithm Chroma and Qdrant use under the hood) • Write-ahead log for crash recovery, every mutation is persisted before it hits memory (still need some refurbishing , i can't lie😂) • Page-based namespacing so each collection has its own independent index • Two-level async locking, global for structural operations, per-page for vector writes • FastAPI HTTP layer with Pydantic validation throughout I started this project because I wanted to actually understand how vector databases work, not just use them. The WAL pattern I landed on mirrors what Kafka, RocksDB, and etcd do. I derived it from first principles before I realized that. That was a good day 😄. This is the first post in a series. I'll be building in public as VectKV grows, snapshots, WAL compaction, delete support and authentication. If you're curious about database internals, vector search, or just enjoy watching something get built from zero, please follow along. GitHub: https://lnkd.in/eHypbVjv #Python #VectorDatabase #BuildingInPublic #DatabaseInternals #MachineLearning #OpenSource

Learning never stops. Over the last weeks we’ve been diving deep into Python, SQL, and NoSQL – building small projects, breaking things on purpose, and then fixing them again. It’s a great way to understand not only how to write queries and scripts, but also how data actually flows through real applications. Step by step, it’s starting to connect: Python for logic and automation, SQL for structured data, and NoSQL for flexible, modern workloads. Looking forward to turning this practice into real‑world projects soon. https://lnkd.in/dcPkK-hX #sql #nosql #python

How to build a sub-100ms live trading pipeline in Python (without writing C++). ⚡ If your tick-to-trade latency is over 500ms, your alpha is already decaying. Python is often called "too slow" for HFT, but the bottleneck is usually bad architecture, not the language. Here is the exact stack I use to keep execution ultra-fast: 1️⃣ Drop REST APIs: Use ZeroMQ for asynchronous, microsecond-level message passing between microservices. 2️⃣ Kill Disk I/O: Store the live order book and tick data entirely in Redis (in-memory) for zero-latency retrieval. 3️⃣ Stream, Don’t Poll: Use direct WebSocket integrations (XTS / Zerodha) instead of requesting data via REST. 4️⃣ Fast DataFrames: Swap Pandas for Polars on the hot path to crunch rolling spreads and time-series data instantly. Finding the strategy is math. Executing it before everyone else is pure engineering. 🛠️ Question for the developers: Are you team Pandas or team Polars for time-series data? Let me know below! 👇 #QuantDev #Python #SystemDesign #SoftwareArchitecture #HighFrequencyTrading #Redis #ZeroMQ #LowLatency #Polars #BackendEngineering

The biggest friction in learning SQL and Python isn't the concepts — it's the setup. Installing databases, configuring environments, debugging Docker containers, troubleshooting driver conflicts. Most beginners spend more time setting up than actually practicing. That's the problem In-Browser Practice on Let's Data Science eliminates entirely. 1,584 SQL and Python coding challenges run directly in your browser. Write your query or script, hit run, and get graded in milliseconds. No local installation, no environment configuration, no "it works on my machine" issues. What makes this different from a generic code playground: → 15 real industry datasets modeled after companies like Amazon, Google, Meta, Netflix, and LinkedIn — not contrived textbook examples → 4 difficulty levels from Easy to Expert, so you can progress at your own pace → Problems tagged by company name, letting you practice the exact style of questions asked at specific employers → Instant automated grading that checks your output against expected results — not just "does it run," but "is it correct" Whether you're preparing for a technical interview next week or building SQL fluency from scratch, the ability to open a browser tab and immediately start solving real-world problems removes every excuse between you and practice. Try any problem — many are free to attempt: https://lnkd.in/gYW7SyFH #DataScience #SQL #Python #LetsDataScience

Your Python Code Consuming Too Much Memory? Today, I explored a fundamental concept in NumPy that many of us often overlook: manual data type (dtype) . While NumPy is naturally more efficient than standard Python arrays, the way we define our data plays a massive role in actual performance. I recently followed a lecture by Respected Sir Zafar Iqbal on this topic, and it changed how I look at memory management in Data Science/ML. Here are my three key takeaways from today's practice: 1. The "Default" Memory Waste When we create an array without specifying a data type, Python often assigns the maximum possible size, such as int64, by default. If your data consists of small numbers (like 1 to 100), using int64 is a waste of resources. By simply defining dtype=np.int8, you can perform the same operations while using significantly less memory. 2. The Out-of-Bounds Trap Every data type has a specific boundary. For instance, int8 can only store values between -128 and 127. If you try to store a number like 130 in an int8 array, you will encounter an "out of bounds" error. In such cases, moving to int16 or int32 provides the necessary range while still being more efficient than the 64-bit default. 3. The Cost of "Object" Flexibility NumPy allows us to mix different types, like strings, integers, and floats, by using dtype=object. While this offers flexibility, it comes at a price: you lose the famous speed advantage that makes NumPy so powerful. For high-performance computing, keeping your data homogeneous is essential. Pro Tip: When working with large datasets, always use the .nbytes attribute to check exactly how much memory your array is consuming. Making small adjustments to your data types can transform a heavy, slow program into a super-efficient one. I am curious to hear from other data professionals: Do you usually stick with the default settings, or do you prefer manual control over your memory usage? Let me know in the comments. #Python #DataScience #NumPy #CodingLife #LearningEveryday #MachineLearning #Efficiency

You won’t master SQL in a month ❌ You won’t master Python in a month ❌ You won’t master PySpark in a month ❌ But here’s what actually works 👇 --- 🔷 SQL 💻 → Solve 1 problem every day Resources → LeetCode, HackerRank, StrataScratch --- 🔷 Python 🐍 → Write scripts on weekends Resources → Codebasics, CodeWithHarry --- 🔷 PySpark ⚡ → Spend 30 mins daily understanding concepts Resources → Darshil Parmar, Deepak Goyal, Shubham Wadekar --- ▪️You don’t need to study like crazy ❌ ▪️You just need to improve a little every day 💡 --- Here’s the truth most people ignore 👇 ▪️1.00^365 = 1 ▪️1.01^365 = 37.7 🚀 --- Do nothing → you stay the same ❌ Improve 1% daily → massive growth 📈 --- 🔹Small steps 🔹Every day That’s your real advantage 🧠🔥 --- 🔸Save this 🔸Stay consistent 🔸Trust the process 🚀 --- #dataengineering #sql #python #pyspark #learningjourney #consistency #careergrowth

Day 7 - I built a live Weather API in Python. It runs for free. Forever. ⠀ 🚀TechFromZero Series - FastAPIFromZero ⠀ This isn't a Hello World. It's a real async REST API with a cloud database: 📐 OpenWeather → FastAPI (Render) → motor → MongoDB Atlas (free) ⠀ 🌐 Try it live: https://lnkd.in/dfrggisJ ⠀ 🔗 The full code (with step-by-step commits you can follow): https://lnkd.in/d8S2AmcP ⠀ 🧱 What I built (step by step): 1️⃣ Project scaffold — venv, requirements.txt, .env.example 2️⃣ FastAPI app — CORS, lifespan hook, health check endpoint 3️⃣ Async MongoDB connection — motor client, shared connection pool 4️⃣ Pydantic schemas — auto-validate input, auto-generate Swagger docs 5️⃣ OpenWeatherMap client — async httpx fetch, clean error handling 6️⃣ Weather endpoints — POST /weather, GET /weather, filter by city, delete, aggregation stats 7️⃣ Render deploy config — render.yaml, $PORT, env vars as code 8️⃣ README — full beginner guide with architecture diagram ⠀ 💡 Every file has detailed comments explaining WHY, not just what. Written for any beginner who wants to learn FastAPI by reading real code — with full clarity on each step. ⠀ 👉 If you're a beginner learning FastAPI, clone it and read the commits one by one. Each commit = one concept. Each file = one lesson. Built from scratch, so nothing is hidden. ⠀ 🔥 This is Day 7 of a 50-day series. A new technology every day. Follow along! ⠀ 🌐 See all days: https://lnkd.in/dhDN6Z3F ⠀ #TechFromZero #Day7 #FastAPI #Python #MongoDB #MongoDBAtlas #Render #AsyncPython #LearnByDoing #OpenSource #BeginnerGuide #100DaysOfCode #CodingFromScratch

🏗️ Building the Backbone: From Python Classes to Persistent Data "The best way to learn is to build, break, and fix. 🛠️ Over the last few days, I’ve been architecting the backend for my FastAPI TodoApp. It’s been a journey of connecting the dots: Defining the Schema: Using SQLAlchemy to turn Python classes into database tables. Establishing the Link: Setting up the engine and SessionLocal to bridge the gap between my app and SQLite. Overcoming Hurdles: Navigating Windows environment variables and mastering the SQLite3 CLI to verify data integrity. The foundation is now officially set. By calling models.Base.metadata.create_all(engine), my app now automatically generates its own database environment on startup. Next stop: Developing the CRUD API endpoints to bring this data to life! 🚀" #Python #FastAPI #SQLAlchemy #BackendDevelopment #CleanCode #SoftwareEngineering

🎉 7 million downloads in a single month is more than just a growth metric. It’s a clear signal that the industry is shifting its focus toward schema-driven reliability 👌 At its core, Ariadne was born from a specific need: a library that stays out of the developer's way while enforcing the rigor required for enterprise-grade APIs. We believed that GraphQL in Python should be "Pythonic" – simple, flexible, and pythonic – without sacrificing the type-safety that complex systems demand. Crossing the 7M monthly download mark proves that the developer community is prioritizing these same values. In an era of increasing architectural complexity, the demand for correctness, speed, and scalability has never been higher. To the teams building production APIs and complex platforms with Ariadne: thank you for trusting us to help build these standards 🙌 👉 Find out more about Ariadne: https://lnkd.in/d2__vaUf We remain committed to evolving the Python GraphQL ecosystem together. #OpenSource #GraphQL #Python #Ariadne #DeveloperTools #API #MirumeeLabs #DevCommunity #SoftwareEngineering

"Excited to share my Final Project for Advanced Database course!” I built a Film Popularity & Rental Demand Prediction System using Django, PostgreSQL, and Machine Learning. What the system does: 1. Analyzes which films are the most rented based on historical data 2. Identifies which film genres generate the most revenue 3. Predicts whether a new film will have High or Low demand using Random Forest Classifier Tech Stack: - Django (Python) - PostgreSQL - Random Forest Classifier (Scikit-learn) - Chart.js for visualization - ETL Pipeline with 3 commands The system processes 958 films and 16 categories from the DVD Rental Database, stores results in OLAP tables, and provides real-time prediction through an interactive dashboard. #Django #MachineLearning #DataAnalytics #Python #PostgreSQL #ETL #OLAP #StudentProject #AdvancedDatabase