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30-Day SQL and Azure SQL for Data Engineering Master Guide

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  For 4–10 Years Experienced Data Engineers Objective This guide is designed to: Build strong SQL fundamentals Develop advanced query writing skills Build real-time problem-solving capability Understand Azure SQL deeply Learn production-level SQL optimization Prepare for senior Data Engineering interviews Target Audience: Data Engineers SQL Developers Azure Data Engineers ETL Developers BI Engineers Daily Time Commitment: 3 Hours Per Day 30 Days Total Learning Strategy: 20% Theory 80% Hands-On SQL Coding Goal: Write optimized SQL queries Solve real-time business problems Handle production SQL incidents Build scalable ETL logic Understand warehouse design Optimize millions/billions of rows DAILY LEARNING STRUCTURE Hour 1 – Learn Concepts Focus on: WHY SQL concepts exist Query execution understanding Real-time usage Optimization mindset Avoid: Blind memorization Watching endless tutorials Hour 2 – Coding Practice Focus on: Writing queries manually Solving analytical problems Window f...
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15-Day Microsoft Fabric for Data Engineering Master Guide

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For 4–10 Years Experienced Data Engineers Objective This guide is designed to: Build strong Microsoft Fabric fundamentals Understand unified analytics architecture Learn OneLake and Lakehouse concepts Build enterprise-level data engineering understanding Prepare for modern cloud analytics interviews Understand Fabric ecosystem integration Target Audience: Data Engineers Azure Data Engineers Fabric Engineers BI Engineers Analytics Engineers Cloud Data Platform Engineers Daily Time Commitment: 3 Hours Per Day 15 Days Total Learning Strategy: 20% Theory 80% Hands-On Practice Goal: Understand Microsoft Fabric architecture deeply Build Lakehouse-based pipelines Integrate engineering + analytics workflows Handle enterprise data platforms Build scalable modern analytics solutions Daily Learning Structure Hour 1 – Learn Concepts Focus on: Understanding WHY Fabric exists Unified analytics understanding Enterprise architecture patterns Lakehouse implementation concepts Avoid: Memorizing UI click...
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7-Day Kafka Intermediate Guide for Data Engineering

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  Beginner to Intermediate Level (Enough for Starting Stage) Objective This guide is designed for: Data Engineers PySpark Developers ETL Developers Streaming Beginners Goal: Understand Kafka architecture Learn real-time streaming basics Build producer-consumer understanding Understand enterprise streaming flow Prepare for beginner/intermediate Kafka interviews Daily Time Commitment: 2 to 3 Hours 7 Days Total Learning Strategy: 30% Theory 70% Hands-On Practice SECTION 1 – WHAT IS KAFKA Apache Kafka is a distributed event streaming platform. Purpose: Real-time data streaming Message processing Event-driven architecture High-throughput data pipelines WHY KAFKA IS USED Problems Kafka Solves: Real-time ingestion Decoupled systems High-volume event processing Streaming analytics Fault-tolerant messaging REAL-TIME EXAMPLES Kafka Use Cases: Banking transactions Website clickstreams IoT devices Fraud detection Call center streaming Log processing Real-time dashboards SECTION 2 – KAFKA ARCHI...
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15-Day Power BI for Data Engineering and Analytics Master Guide

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  For 4–10 Years Experienced Data Engineers / BI Engineers / Data Analysts Objective This guide is designed to: Build strong Power BI fundamentals Understand reporting and dashboard development Learn DAX and data modeling deeply Build real-time analytical dashboards Understand enterprise reporting architecture Prepare for senior-level interviews Target Audience: Data Engineers BI Developers Power BI Developers Reporting Engineers Data Analysts Analytics Engineers Daily Time Commitment: 3 Hours Per Day 15 Days Total Learning Strategy: 20% Theory 80% Hands-On Practice Goal: Build interactive dashboards Understand DAX deeply Create enterprise reports Build optimized data models Handle real-time reporting scenarios Daily Learning Structure Hour 1 – Learn Concepts Focus on: Understanding WHY Power BI concepts exist Reporting architecture understanding Data modeling concepts Real-time analytics thinking Avoid: Memorizing visuals blindly Watching endless tutorials Hour 2 – Hands-On Develo...
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15-Day Azure Data Factory (ADF) for Data Engineering Master Guide

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For 4–10 Years Experienced Data Engineers Objective This guide is designed to: Build strong Azure Data Factory fundamentals Understand enterprise ETL orchestration Learn real-time pipeline implementations Master integration and automation concepts Prepare for senior-level interviews Build scalable cloud data engineering mindset Target Audience: Data Engineers Azure Data Engineers ETL Developers Cloud Data Platform Engineers Integration Engineers Daily Time Commitment: 3 Hours Per Day 15 Days Total Learning Strategy: 20% Theory 80% Hands-On Practice Goal: Build enterprise-grade ETL pipelines Understand orchestration deeply Handle production workflows Implement incremental processing Build scalable cloud integrations Daily Learning Structure Hour 1 – Learn Concepts Focus on: Understanding WHY ADF exists Pipeline orchestration concepts Integration architecture Real-time enterprise use cases Avoid: Memorizing UI clicks blindly Watching endless tutorials Hour 2 – Hands-On Development Focus ...
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15-Day Databricks for Data Engineering Master Guide

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  For 4–10 Years Experienced Data Engineers Objective This guide is designed to: Build strong Databricks fundamentals Understand Lakehouse architecture Learn real-time enterprise implementations Master Databricks workflows and optimization Build scalable ETL pipelines Prepare for senior-level interviews Target Audience: Data Engineers Azure Databricks Engineers PySpark Developers Big Data Engineers ETL Developers Cloud Data Platform Engineers Daily Time Commitment: 3 Hours Per Day 15 Days Total Learning Strategy: 20% Theory 80% Hands-On Practice Goal: Understand Databricks architecture deeply Build production-grade ETL pipelines Optimize Spark workloads Implement Delta Lake solutions Handle enterprise-scale data engineering workflows Daily Learning Structure Hour 1 – Learn Concepts Focus on: Understanding WHY Databricks is used Architecture understanding Real-time implementation patterns Optimization strategies Avoid: Memorizing notebook syntax blindly Watching endless tutorials Ho...
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