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This repository showcases my comprehensive learning journey through modern big data technologies, cloud platforms, and distributed computing systems. Each folder contains hands-on projects and implementations demonstrating practical skills acquired during the bootcamp.\n\n## 📚 Course Overview\n\nThis intensive bootcamp provided profound understanding of big data concepts, from foundational distributed systems to modern cloud-native solutions. The course emphasized hands-on experience with industry-standard tools and real-world project implementations.\n\n## 🛠 Technologies \u0026 Tools Mastered\n\n### **Distributed Computing \u0026 Storage**\n- **Hadoop Ecosystem**: HDFS, YARN, MapReduce\n- **Apache Spark**: PySpark, RDDs, DataFrames, Spark SQL\n- **Apache Hive**: HQL, Metastore, Derby DB\n- **Google Cloud Dataproc**: Cluster management and distributed processing\n\n### **Real-Time Data Streaming**\n- **Apache Kafka**: Producer/Consumer patterns, Confluent Cloud\n- **Stream Processing**: Real-time data ingestion and processing\n\n### **Containerization \u0026 Orchestration**\n- **Docker**: Container creation, Dockerfile, multi-container applications\n- **Docker Compose**: Service orchestration and networking\n- **Apache Airflow**: Workflow orchestration, DAGs, task scheduling\n\n### **Cloud Platforms**\n- **Google Cloud Platform (GCP)**: Dataproc, BigQuery, Cloud Storage\n- **Microsoft Azure**: Data Factory, Data Lake Storage, Synapse Analytics, Databricks\n\n### **Databases \u0026 Data Storage**\n- **MySQL**: Relational database operations and data ingestion\n- **MongoDB**: NoSQL document database integration\n- **SQLite**: Lightweight database for development and testing\n\n### **Programming \u0026 Development**\n- **Python**: Core programming, data manipulation, ETL processes\n- **PySpark**: Distributed data processing and analytics\n- **SQL**: Advanced querying, data analysis, and reporting\n\n## 🗂 Repository Structure\n\n### **Core Learning Modules**\n- [`Python/`](./Python/) - Python fundamentals, pandas, numpy, OOP concepts\n- [`Apache_Spark_Pyspark_Jobs/`](./Apache_Spark_Pyspark_Jobs/) - Spark applications and data analysis\n- [`Apache_Kafka_Streamline/`](./Apache_Kafka_Streamline/) - Kafka streaming implementations\n- [`MySQL/`](./MySQL/) - SQL queries and database operations\n- [`SQLite/`](./SQLite/) - Local database development and logging\n\n### **Cloud \u0026 Orchestration Projects**\n- [`Azure_Synapse_SQL_Queries/`](./Azure_Synapse_SQL_Queries/) - Azure Synapse Analytics implementations\n- [`ADLS_Medalian_Structured_Storage/`](./ADLS_Medalian_Structured_Storage/) - Medallion architecture on Azure Data Lake\n- [`Airflow_Orchestrations/`](./Airflow_Orchestrations/) - Workflow orchestration and ETL pipelines\n- [`Docker_Deployments/`](./Docker_Deployments/) - Containerized applications and services\n\n### **Data Processing \u0026 Analytics**\n- [`Databricks_Data_Processing/`](./Databricks_Data_Processing/) - Advanced analytics on Databricks\n- [`GCP_Pyspark_Data_Analysis/`](./GCP_Pyspark_Data_Analysis/) - Google Cloud data processing\n- [`Data_Ingestion_MySQL_MongoDB/`](./Data_Ingestion_MySQL_MongoDB/) - Multi-source data ingestion\n\n### **Pipeline \u0026 Integration**\n- [`ADF_Data_Ingestion_Pipeline/`](./ADF_Data_Ingestion_Pipeline/) - Azure Data Factory pipelines\n- [`ccloud-python-client/`](./ccloud-python-client/) - Confluent Cloud integration\n\n## 🏗 Key Learning Concepts\n\n### **Distributed Systems Architecture**\n- **Hadoop File System (HDFS)**: Understanding data distribution across worker nodes\n- **Master-Worker Architecture**: How master nodes coordinate with worker nodes for distributed processing\n- **Cluster Management**: Hands-on experience with Google Dataproc clusters\n- **Resource Management**: YARN for resource negotiation and parallel processing\n\n### **Data Processing Evolution**\n- **MapReduce**: Legacy distributed processing framework and its limitations\n- **Apache Spark**: Modern alternative with in-memory processing capabilities\n- **Spark Components**: Jobs, Tasks, Stages, Partitions, and execution optimization\n\n### **Data Storage Strategies**\n- **Medallion Architecture**: Bronze, Silver, Gold data layers\n- **Data Lake Storage**: Structured and unstructured data management\n- **Metastore Management**: Hive for SQL table metadata storage\n\n### **Modern Data Pipeline Architecture**\n- **Real-Time Streaming**: Kafka for continuous data ingestion\n- **Batch Processing**: Scheduled ETL workflows\n- **Workflow Orchestration**: Airflow DAGs for complex pipeline management\n- **Containerization**: Docker for consistent deployment environments\n\n## 🎯 Hands-On Projects\n\n### **End-to-End Azure Cloud Project**\nImplemented a comprehensive data pipeline featuring:\n- **Data Ingestion**: GitHub HTTP requests and MongoDB integration via Azure Data Factory\n- **Storage**: Azure Data Lake Storage with Medallion architecture\n- **Processing**: Azure-powered Databricks for data transformation\n- **Analytics**: Azure Synapse for external table creation and analysis\n- **Serving**: Gold layer data ready for downstream consumption by Data Scientists and Analysts\n\n## 🎯 Key Production Projects\n\n### **Real-Time Streaming Pipeline**\n• **Engineered** Apache Kafka producer/consumer architecture with **topic subscription** for high-throughput real-time message processing and data streaming at enterprise scale.\n\n### **Workflow Orchestration Platform**\n• **Implemented** Apache Airflow DAGs for **cyclical ETL workflows**, successfully deployed to production environments including Astro Cloud and AWS with automated scheduling.\n\n### **Containerized Data Platform**\n• **Architected** Docker multi-container solution integrating **Kafka + PostgreSQL + API ingestion**, deployed to Docker Hub for scalable data processing and analytics.\n\n### **End-to-End Azure Cloud Pipeline**\n• **Delivered** production-grade data pipeline using **ADF + ADLS + Databricks + Synapse**, implementing medallion architecture for enterprise data lake solutions.\n\n### **Distributed Processing \u0026 Analytics Platform**\n• **Orchestrated** HDFS data migration from local to **Google Cloud Storage + Dataproc**, leveraging Apache Spark and PySpark for parallel processing of 4+ synthetic e-commerce datasets.\n\n## 📊 Data Analysis \u0026 Visualization\n\n### **E-commerce Data Analysis**\n- **Platform**: Databricks and Google Cloud\n- **Dataset**: Olist Brazilian E-commerce dataset\n- **Techniques**: Data transformation, statistical analysis, and visualization\n- **Deliverables**: Comprehensive insights and business intelligence reports\n\n## 🔧 Development Environment\n\n- **Languages**: Python, SQL, HQL\n- **IDEs**: Jupyter Notebook, Databricks Notebooks, VS Code\n- **Version Control**: Git/GitHub\n- **Cloud Platforms**: GCP, Azure\n- **Containerization**: Docker, Docker Compose\n\n## 📈 Skills Acquired\n\n### **Technical Skills**\n- Distributed data processing and parallel computing\n- Real-time and batch data pipeline development\n- Cloud-native application development\n- Container orchestration and deployment\n- Advanced SQL and NoSQL database management\n\n### **Architecture \u0026 Design**\n- Microservices architecture design\n- Data lake and data warehouse design patterns\n- ETL/ELT pipeline architecture\n- Scalable system design principles\n\n### **DevOps \u0026 Operations**\n- Infrastructure as Code concepts\n- Continuous integration principles\n- Monitoring and logging implementations\n- Performance optimization strategies\n\n## 🚀 Future Learning Goals\n\n- Machine Learning pipeline integration\n- DataOps and MLOps implementations\n- Advanced stream processing patterns\n\n## 📞 Contact\n\nFeel free to explore the projects and reach out for discussions on big data engineering, cloud architecture, or distributed systems!\n\n## 🙏 Acknowledgement\n\n- Udemy: [Big Data Engineering - Azure, GCP, AWS](https://www.udemy.com/share/10cMDh3@TbwMYKRyzF_nXnQ7M_xxvEvWFBo3RwmhWer_pVyNMNL4B8qgtLYxIFw1JIcRqkrKDQ==/)\n\n---\n\n*This repository represents my journey through modern big data engineering practices, showcasing hands-on experience with industry-standard tools and real-world project implementations.* \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdhana5982%2Fbig_data_engineering_azure_gcp_aws","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdhana5982%2Fbig_data_engineering_azure_gcp_aws","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdhana5982%2Fbig_data_engineering_azure_gcp_aws/lists"}