📌 About Me: I am a Data Solution Engineer with 12+ years of experience in Big Data, Cloud (Azure & AWS), and AI-driven data solutions. Passionate about building scalable ETL pipelines, optimizing Spark jobs, and leveraging AI for data automation. I have worked across industries like finance, gaming, automotive, and healthcare, helping businesses make data-driven decisions efficiently. 📌 What I Write About: PySpark & Big Data Processing 🏗️ Optimizing ETL & Data Pipelines ⚡ Cloud Engineering (Azure & AWS) ☁️ Streaming & Real-Time Data (Kafka, Spark Streaming) 📡 AI & Machine Learning in Data Engineering 🤖 📌 Why Follow Me? I share real-world data engineering challenges and hands-on solutions to help fellow engineers overcome bottlenecks and optimize data workflows. Let’s build robust, scalable, and cost-efficient data systems together! Follow for updates on cutting-edge data engineering topics!
Freelance & Consulting (Big Data, Cloud, PySpark, ETL Optimization)
Apr 27, 2025 · 1 min read · Are you curious about how Azure Data Factory (ADF) efficiently handles massive data movement across diverse environments—both cloud and on-premises? This visual simplifies the inner workings of ADF Copy Activity Scaling Control & Orchestration ADF...
Join discussionApr 27, 2025 · 2 min read · If you’ve ever worked with a data lake, you know how quickly it can turn into a “data swamp.”Messy, Unstructured, Hard to trust, Harder to scale. That’s where Medallion Architecture comes in — and when combined with Databricks, it becomes an absolu...
Join discussionApr 16, 2025 · 1 min read · Overview In this session, we’ll walk through building a simple ETL (Extract, Transform, Load) pipeline using PySpark Steps Involved Setup Spark Environment Read Data from CSV Cleanse and Transform the CSV data Write final results to target locati...
Join discussionApr 10, 2024 · 1 min read · Scenario You have a large CSV file (100GB+ of data) with millions of records. Loading the file without optimization causes memory issues and slow performance. Solution: Use Partitioning & Parquet for Faster Processing Step 1: Read the Large CSV in Py...
Join discussionApr 6, 2024 · 1 min read · Scenario Your dataset contains duplicate customer records. You need to remove duplicates based on the latest timestamp. Solution: Use dropDuplicates() & Window Functions Step 1: Sample Data from pyspark.sql.functions import col from pyspark.sql impor...
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