I recently built a data pipeline that automatically tracks and visualizes real-time weather data. The project follows an ELT (Extract, Load, Transform) workflow to keep data moving quickly and accurately from the source to the final dashboard. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: • 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻: A Python script pulls live weather data from an API every 5 minutes. • 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: The raw data is immediately loaded into a PostgreSQL database. • 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗼𝗿𝘁𝗶𝗻𝗴: I use dbt to transform raw data into structured tables for analysis: • 𝘀𝘁𝗴_𝘄𝗲𝗮𝘁𝗵𝗲𝗿_𝗱𝗮𝘁𝗮: The staging table where raw API data is cleaned, validated, and prepared for further processing. • 𝘄𝗲𝗮𝘁𝗵𝗲𝗿_𝗿𝗲𝗽𝗼𝗿𝘁: A refined table designed for real-time monitoring with clear, analysis-ready weather insights. • 𝗱𝗮𝗶𝗹𝘆_𝗮𝘃𝗲𝗿𝗮𝗴𝗲: An aggregated table that summarizes daily weather metrics to track trends over time. • 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Apache Airflow orchestrates the entire process. • 𝗟𝗶𝘃𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱: Apache Superset displays results with a 5-minute auto-refresh. • 𝗦𝗲𝘁𝘂𝗽: Fully containerized using Docker for easy deployment. 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • 𝗡𝗲𝗮𝗿-𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲: Data updates every 5 minutes. • 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲: Prevents duplicates and ensures high-quality data. • 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁: ELT enables scalable transformations inside the database. This project helped me build a complete, automated data system from scratch. #DataEngineering #ELT #Python #SQL #Airflow #Docker #DataPipeline #WeatherUpdate

🚀 Built an End-to-End Data Pipeline using API & SQL Server! Excited to share my recent hands-on project where I built a complete data pipeline from scratch 👇 🔹 What I did: 1. Source Database (SQL Server)   ↓ 2. Create API using FastAPI   ↓ 3. Expose endpoint (/data)   ↓ 4. Call API using Python (requests)   ↓ 5. Get data in JSON format   ↓ 6. Connect to Target SQL Server   ↓ 7. Auto-create table (if not exists)   ↓ 8. Insert data into target table   ↓ 9. Verify data in SSMS 🔹 Tech Stack: Python | FastAPI | SQL Server | pyodbc | requests 🔹 Key Learnings: 💡 How APIs act as a bridge between systems   💡 Converting JSON data into structured format   💡 Building real-world ETL pipelines   💡 Automating data movement without manual intervention  This project helped me understand how real-world data engineering pipelines work — from data extraction to loading 🚀 Looking forward to building more such projects and improving my skills! #DataEngineering #Python #FastAPI #SQLServer #ETL #DataPipeline #LearningInPublic #100DaysOfData #BuildingInPublic

Most data analysts on my team spent more time writing SQL than actually analysing data. So I built a fix — without touching our existing Superset setup. It's called a Text-to-SQL Sidecar: a standalone FastAPI microservice that sits alongside Apache Superset and turns plain English into validated, safe SQL. You ask: "which products had the highest return rate last quarter?" It generates, validates, and executes the SQL — then hands the results back. A few things I was deliberate about: → AST-level SQL validation (not string matching — trivially bypassable) → Per-database table allowlists so the LLM can only touch what it's supposed to → Schema caching so we're not hammering the DB on every request → LLM-agnostic design — swap the endpoint URL, change the model → Reasoning traces returned alongside SQL so analysts can actually trust the output Superset never needs to know it exists. It just receives SQL. I wrote up the full implementation — architecture, code walkthrough, and the design decisions that make it production-ready. Link in the comments 👇 #DataEngineering #AI #SQL #FastAPI #ApacheSuperset #LLM #Python

We cut peak-time dashboard resource usage by ~50% without adding new servers. Here’s the breakdown. 🚀 As traffic grew, one of our internal dashboards started slowing down exactly when usage was highest. Response times increased, database load spiked, and unnecessary queries were consuming resources. The issue wasn’t infrastructure. It was application-level inefficiency. The Challenge The dashboard was making repeated database hits while rendering data-heavy views. Classic symptoms: • Slow response times during peak hours • Increased DB utilization • Higher CPU/memory pressure on the app layer After profiling the flow, the root cause was clear: 👉 N+1 query patterns + repeated data fetching logic What I Changed 1️⃣ Consolidated Data Fetching Used Django ORM features like: • select_related() for ForeignKey joins • prefetch_related() for reverse/M2M relationships This ensured related data was fetched in batches instead of per record. 2️⃣ Reduced Repeated Query Execution • Removed queryset evaluations inside loops • Cached reusable datasets during request lifecycle • Avoided duplicate ORM calls across helper methods 3️⃣ Shifted Transformations to Python Once the required data was fetched efficiently, grouping/filtering/manipulation was done in-memory rather than repeatedly querying the DB. 4️⃣ Leaner Payloads Used .values() / targeted field selection where full model objects were unnecessary. The Impact ⚡ • ~50% reduction in resource usage during peak load • Significant drop in DB hits • Faster dashboard response times • Better stability under concurrent traffic 🚀 3 Lessons for Scaling Django Backends Query count matters more than query elegance One clean query repeated 500 times is still expensive. Fetch once, process many Databases should retrieve data. Business logic can often run in memory. Profile peak traffic scenarios Many bottlenecks only appear under real concurrency. Performance wins don’t always come from bigger infra. Sometimes they come from better data flow design. #Django #Python #BackendEngineering #PerformanceOptimization #Scalability #SoftwareEngineering

🚀 Built an End-to-End Data Pipeline using API, Python & SQL Server! Excited to share a hands-on project where I implemented a complete data pipeline across two systems 💻 🔹 Project Overview: ✔ Extracted data from PostgreSQL (Laptop 1) ✔ Exposed data via Django API (JSON format) ✔ Accessed API from another machine (Laptop 2) ✔ Converted JSON → CSV using Python (pandas) ✔ Dynamically created table (no manual schema!) ✔ Loaded data into SQL Server using pyodbc 🔹 Architecture: PostgreSQL → Django API → JSON → Python → CSV → SQL Server 🔹 Key Learnings: 💡 API as a bridge between systems   💡 Handling JSON data in real-world scenarios   💡 Automating schema creation   💡 Cross-machine data transfer   💡 Building end-to-end ETL pipelines  This project gave me practical exposure to how modern data pipelines work in real-world data engineering 🚀 Looking forward to building more scalable and production-ready pipelines! #DataEngineering #Python #SQLServer #FastAPI #Django #ETL #DataPipeline #APIs #LearningInPublic #100DaysOfCode

Just wrapped up an end-to-end data engineering project as part of DataTalksClub’s Data Engineering Zoomcamp Built a pipeline to process GitHub Events data using Python, BigQuery, dbt, and Airflow. Some key things I worked on:   - Designed an ELT pipeline using GCS → BigQuery → dbt   - Implemented dimensional modelling (fact & dimension tables)   - Orchestrated workflows using Apache Airflow (Dockerised)   - Optimised performance using Parquet and partitioning   - Reduced query costs after initially scanning ~200GB per run One of the biggest learnings for me was how small design decisions (like partitioning and materialisation strategy) can have a huge impact on performance and cost. Also got to debug real-world issues like Airflow setup problems, inefficient dbt models, and I/O bottlenecks — which made the learning much more practical. Dashboard: https://lnkd.in/gxgQYVkH Github Repo: https://lnkd.in/gVtVvWR9 I will continue improving this project by adding new features and optimisations over time. Would love to hear any feedback! 🙂 #DataEngineering #BigQuery #ApacheAirflow

In my last post, I introduced PydanTable—Pydantic-native tables, lazy transforms, and Rust-backed execution. Now, let's explore the next layer: SQL. Many data tools follow a familiar pattern for SQL sources: pulling rows into Python, transforming them, and then writing them somewhere else. While this approach works, it becomes cumbersome when dealing with large datasets or when your write target is the same database from which you read. The process of “extracting everything locally” can feel more like a burden than a benefit. PydanTable now offers an optional SQL execution path, allowing you to keep transformations within the database as long as the engine supports them. You only materialize data when you actually need it on the Python side. This shifts the paradigm from classic ETL—Extract, Transform locally, Load—to a more efficient TEL: Transform in SQL, extract locally when needed, then load. The primary advantage is operational efficiency. When your load target is on the same SQL server, you can often bypass the costly step of transferring the entire result set through the application, enabling a direct transition from transformation to loading, with the server handling the heavy lifting. This approach also indicates our future direction: a more intelligent execution strategy for PydanTable. The planner will optimize work on the read side when it is safe and efficient, selecting the best compute resources rather than defaulting to local resources or a single engine that may not be ideal for the task. On the roadmap, we have plans for a MongoDB engine to allow aggregation to remain on the server before extraction or writing back, as well as a PySpark-engine that introduces strong typing to traditional Spark-style operations. I am excited to continue advancing PydanTable beyond merely “strongly typed dataframes” toward strong typing where the data already resides. #DataEngineering #Python #OpenSource #SQL #ETL

Built Clarity because data teams were drowning in tools. One tool for SQL. Another for ETL. Another for dashboards. Another for reporting. None of them talk to each other. So we built one workspace that does it all — and made it AI-native from day one. → Full data lineage — trace every metric back to its source → Governed pipelines with audit trails and role-based access → A semantic layer your whole org trusts as the single source of truth → Query in SQL or plain English — every result is reproducible Full demo coming soon. Built with #Flutter #FastAPI #ClickHouse #Python #FlutterWeb #DataPlatform #Analytics #BuildInPublic #DataScience #SaaS #DataGovernance #RealTimeAnalytics #DataTransparency #DataQuality #TechStartup #DataOps #DataEngineering #DataDriven

Most people use Claude Code like a smarter autocomplete. That's not what it is. If you structure your repo correctly, Claude Code operates more like a disciplined junior engineer — one that reads the docs before touching anything, follows your conventions, guards against dangerous operations, and leaves a clean audit trail after every session. The difference isn't the model. It's the project structure. Here's what actually matters: 1. CLAUDE.md — your AI onboarding doc. Client context, architecture diagram, coding conventions, known gaps. Auto-loaded every session. 2. A session brief (read.md) — what today's focus is, what was decided last time, what's locked. Prevents you repeating the same discovery work twice. 3. Slash commands — package your multi-step workflows as markdown files. /add-bronze-object, /add-gold-transform, /check-pipeline-status. One command, done correctly every time. 4. Hooks — Python scripts that intercept Claude before it runs a bash command or writes a file. Block destructive CLI calls. Catch bad SQL. Surface a git diff on exit. 5. Discovery docs — let Claude query your actual source DB and document what it finds. Real column names, real data patterns, real gotchas. No guesswork in the SQL. I ran this setup on a full Snowflake medallion pipeline — MSSQL source, Bronze → Silver → Gold, 25 objects. 25/25 built. 0 failures. One session. I also wrote a section on prompt pollution — what happens when vague or exploratory prompts silently contaminate your session context and why it's so hard to catch. Worth reading if you use any LLM in your data work. #DataEngineering #SnowflakeDB #ClaudeCode #ETL #ArtificialIntelligence #Python #DataPipeline #MLOps Full article 👇 https://lnkd.in/gc7tAXDA

I migrated our 50GB Pandas pipeline to Polars. The difference shocked me: Our daily ETL was taking 4+ hours and burning through memory like crazy. The team was getting frustrated with constant OOM errors. I'd heard whispers about Polars but was skeptical. Another "revolutionary" tool? 🙄 But desperate times called for desperate measures. Here's what I learned during the 3-week migration: 1. **Memory usage dropped 70%** - Polars' lazy evaluation only loads what it needs 2. **Query optimization is automatic** - No more manual .query() tweaking 3. **Parallel processing works out of the box** - Unlike Pandas' single-threaded operations 4. **The .lazy() API feels familiar** - Most Pandas logic translated smoothly 5. **Arrow backend makes file I/O lightning fast** - Parquet reads went from 20min to 4min ⚡ The real game-changer? Our pipeline now runs in 45 minutes instead of 4+ hours. My manager asked why we didn't switch sooner 😅 The syntax learning curve was maybe 2 days. The performance gains were immediate. Sure, Pandas has a massive ecosystem. But for pure data processing at scale, Polars is becoming my go-to. One warning though - debugging can be trickier with lazy evaluation. Plan accordingly! 🚨 What's been your experience with Polars? Still team Pandas or making the switch? 🤔 #DataEngineering #Python #Polars #Pandas #ETL #DataProcessing #BigData #Performance #DataScience #Analytics #TechMigration #DataPipeline