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https://github.com/dhana5982/big_data_engineering_azure_gcp_aws

Comprehensive Big Data Engineering learning repository featuring hands-on projects with Hadoop, Spark, Kafka, Docker, Airflow, and Azure Cloud. Includes end-to-end data pipelines, real-time streaming, and distributed processing implementations.
https://github.com/dhana5982/big_data_engineering_azure_gcp_aws

amazon-web-services apache-airflow apache-kafka apache-spark azure-cloud-services big-data data-engineering databricks distributed-computing docker docker-compose google-cloud-platform hadoop-ecosystem hive-metastore mongodb mysql pyspark real-time-streaming sqlite3 workflow-orchestration

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Comprehensive Big Data Engineering learning repository featuring hands-on projects with Hadoop, Spark, Kafka, Docker, Airflow, and Azure Cloud. Includes end-to-end data pipelines, real-time streaming, and distributed processing implementations.

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README 

          
# Big Data Engineering Bootcamp - Learning Journey 🚀



Welcome to my Big Data Engineering repository! 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.



## 📚 Course Overview



This 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.



## 🛠 Technologies & Tools Mastered



### **Distributed Computing & Storage**

- **Hadoop Ecosystem**: HDFS, YARN, MapReduce

- **Apache Spark**: PySpark, RDDs, DataFrames, Spark SQL

- **Apache Hive**: HQL, Metastore, Derby DB

- **Google Cloud Dataproc**: Cluster management and distributed processing



### **Real-Time Data Streaming**

- **Apache Kafka**: Producer/Consumer patterns, Confluent Cloud

- **Stream Processing**: Real-time data ingestion and processing



### **Containerization & Orchestration**

- **Docker**: Container creation, Dockerfile, multi-container applications

- **Docker Compose**: Service orchestration and networking

- **Apache Airflow**: Workflow orchestration, DAGs, task scheduling



### **Cloud Platforms**

- **Google Cloud Platform (GCP)**: Dataproc, BigQuery, Cloud Storage

- **Microsoft Azure**: Data Factory, Data Lake Storage, Synapse Analytics, Databricks



### **Databases & Data Storage**

- **MySQL**: Relational database operations and data ingestion

- **MongoDB**: NoSQL document database integration

- **SQLite**: Lightweight database for development and testing



### **Programming & Development**

- **Python**: Core programming, data manipulation, ETL processes

- **PySpark**: Distributed data processing and analytics

- **SQL**: Advanced querying, data analysis, and reporting



## 🗂 Repository Structure



### **Core Learning Modules**

- [`Python/`](./Python/) - Python fundamentals, pandas, numpy, OOP concepts

- [`Apache_Spark_Pyspark_Jobs/`](./Apache_Spark_Pyspark_Jobs/) - Spark applications and data analysis

- [`Apache_Kafka_Streamline/`](./Apache_Kafka_Streamline/) - Kafka streaming implementations

- [`MySQL/`](./MySQL/) - SQL queries and database operations

- [`SQLite/`](./SQLite/) - Local database development and logging



### **Cloud & Orchestration Projects**

- [`Azure_Synapse_SQL_Queries/`](./Azure_Synapse_SQL_Queries/) - Azure Synapse Analytics implementations

- [`ADLS_Medalian_Structured_Storage/`](./ADLS_Medalian_Structured_Storage/) - Medallion architecture on Azure Data Lake

- [`Airflow_Orchestrations/`](./Airflow_Orchestrations/) - Workflow orchestration and ETL pipelines

- [`Docker_Deployments/`](./Docker_Deployments/) - Containerized applications and services



### **Data Processing & Analytics**

- [`Databricks_Data_Processing/`](./Databricks_Data_Processing/) - Advanced analytics on Databricks

- [`GCP_Pyspark_Data_Analysis/`](./GCP_Pyspark_Data_Analysis/) - Google Cloud data processing

- [`Data_Ingestion_MySQL_MongoDB/`](./Data_Ingestion_MySQL_MongoDB/) - Multi-source data ingestion



### **Pipeline & Integration**

- [`ADF_Data_Ingestion_Pipeline/`](./ADF_Data_Ingestion_Pipeline/) - Azure Data Factory pipelines

- [`ccloud-python-client/`](./ccloud-python-client/) - Confluent Cloud integration



## 🏗 Key Learning Concepts



### **Distributed Systems Architecture**

- **Hadoop File System (HDFS)**: Understanding data distribution across worker nodes

- **Master-Worker Architecture**: How master nodes coordinate with worker nodes for distributed processing

- **Cluster Management**: Hands-on experience with Google Dataproc clusters

- **Resource Management**: YARN for resource negotiation and parallel processing



### **Data Processing Evolution**

- **MapReduce**: Legacy distributed processing framework and its limitations

- **Apache Spark**: Modern alternative with in-memory processing capabilities

- **Spark Components**: Jobs, Tasks, Stages, Partitions, and execution optimization



### **Data Storage Strategies**

- **Medallion Architecture**: Bronze, Silver, Gold data layers

- **Data Lake Storage**: Structured and unstructured data management

- **Metastore Management**: Hive for SQL table metadata storage



### **Modern Data Pipeline Architecture**

- **Real-Time Streaming**: Kafka for continuous data ingestion

- **Batch Processing**: Scheduled ETL workflows

- **Workflow Orchestration**: Airflow DAGs for complex pipeline management

- **Containerization**: Docker for consistent deployment environments



## 🎯 Hands-On Projects



### **End-to-End Azure Cloud Project**

Implemented a comprehensive data pipeline featuring:

- **Data Ingestion**: GitHub HTTP requests and MongoDB integration via Azure Data Factory

- **Storage**: Azure Data Lake Storage with Medallion architecture

- **Processing**: Azure-powered Databricks for data transformation

- **Analytics**: Azure Synapse for external table creation and analysis

- **Serving**: Gold layer data ready for downstream consumption by Data Scientists and Analysts



## 🎯 Key Production Projects



### **Real-Time Streaming Pipeline**

• **Engineered** Apache Kafka producer/consumer architecture with **topic subscription** for high-throughput real-time message processing and data streaming at enterprise scale.



### **Workflow Orchestration Platform**

• **Implemented** Apache Airflow DAGs for **cyclical ETL workflows**, successfully deployed to production environments including Astro Cloud and AWS with automated scheduling.



### **Containerized Data Platform**

• **Architected** Docker multi-container solution integrating **Kafka + PostgreSQL + API ingestion**, deployed to Docker Hub for scalable data processing and analytics.



### **End-to-End Azure Cloud Pipeline**

• **Delivered** production-grade data pipeline using **ADF + ADLS + Databricks + Synapse**, implementing medallion architecture for enterprise data lake solutions.



### **Distributed Processing & Analytics Platform**

• **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.



## 📊 Data Analysis & Visualization



### **E-commerce Data Analysis**

- **Platform**: Databricks and Google Cloud

- **Dataset**: Olist Brazilian E-commerce dataset

- **Techniques**: Data transformation, statistical analysis, and visualization

- **Deliverables**: Comprehensive insights and business intelligence reports



## 🔧 Development Environment



- **Languages**: Python, SQL, HQL

- **IDEs**: Jupyter Notebook, Databricks Notebooks, VS Code

- **Version Control**: Git/GitHub

- **Cloud Platforms**: GCP, Azure

- **Containerization**: Docker, Docker Compose



## 📈 Skills Acquired



### **Technical Skills**

- Distributed data processing and parallel computing

- Real-time and batch data pipeline development

- Cloud-native application development

- Container orchestration and deployment

- Advanced SQL and NoSQL database management



### **Architecture & Design**

- Microservices architecture design

- Data lake and data warehouse design patterns

- ETL/ELT pipeline architecture

- Scalable system design principles



### **DevOps & Operations**

- Infrastructure as Code concepts

- Continuous integration principles

- Monitoring and logging implementations

- Performance optimization strategies



## 🚀 Future Learning Goals



- Machine Learning pipeline integration

- DataOps and MLOps implementations

- Advanced stream processing patterns



## 📞 Contact



Feel free to explore the projects and reach out for discussions on big data engineering, cloud architecture, or distributed systems!



## 🙏 Acknowledgement



- Udemy: [Big Data Engineering - Azure, GCP, AWS](https://www.udemy.com/share/10cMDh3@TbwMYKRyzF_nXnQ7M_xxvEvWFBo3RwmhWer_pVyNMNL4B8qgtLYxIFw1JIcRqkrKDQ==/)



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*This repository represents my journey through modern big data engineering practices, showcasing hands-on experience with industry-standard tools and real-world project implementations.*