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.
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.
- 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
- Apache Kafka: Producer/Consumer patterns, Confluent Cloud
- Stream Processing: Real-time data ingestion and processing
- Docker: Container creation, Dockerfile, multi-container applications
- Docker Compose: Service orchestration and networking
- Apache Airflow: Workflow orchestration, DAGs, task scheduling
- Google Cloud Platform (GCP): Dataproc, BigQuery, Cloud Storage
- Microsoft Azure: Data Factory, Data Lake Storage, Synapse Analytics, Databricks
- MySQL: Relational database operations and data ingestion
- MongoDB: NoSQL document database integration
- SQLite: Lightweight database for development and testing
- Python: Core programming, data manipulation, ETL processes
- PySpark: Distributed data processing and analytics
- SQL: Advanced querying, data analysis, and reporting
Python/- Python fundamentals, pandas, numpy, OOP conceptsApache_Spark_Pyspark_Jobs/- Spark applications and data analysisApache_Kafka_Streamline/- Kafka streaming implementationsMySQL/- SQL queries and database operationsSQLite/- Local database development and logging
Azure_Synapse_SQL_Queries/- Azure Synapse Analytics implementationsADLS_Medalian_Structured_Storage/- Medallion architecture on Azure Data LakeAirflow_Orchestrations/- Workflow orchestration and ETL pipelinesDocker_Deployments/- Containerized applications and services
Databricks_Data_Processing/- Advanced analytics on DatabricksGCP_Pyspark_Data_Analysis/- Google Cloud data processingData_Ingestion_MySQL_MongoDB/- Multi-source data ingestion
ADF_Data_Ingestion_Pipeline/- Azure Data Factory pipelinesccloud-python-client/- Confluent Cloud integration
- 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
- 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
- Medallion Architecture: Bronze, Silver, Gold data layers
- Data Lake Storage: Structured and unstructured data management
- Metastore Management: Hive for SQL table metadata storage
- 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
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
• Engineered Apache Kafka producer/consumer architecture with topic subscription for high-throughput real-time message processing and data streaming at enterprise scale.
• Implemented Apache Airflow DAGs for cyclical ETL workflows, successfully deployed to production environments including Astro Cloud and AWS with automated scheduling.
• Architected Docker multi-container solution integrating Kafka + PostgreSQL + API ingestion, deployed to Docker Hub for scalable data processing and analytics.
• Delivered production-grade data pipeline using ADF + ADLS + Databricks + Synapse, implementing medallion architecture for enterprise data lake solutions.
• 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.
- 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
- Languages: Python, SQL, HQL
- IDEs: Jupyter Notebook, Databricks Notebooks, VS Code
- Version Control: Git/GitHub
- Cloud Platforms: GCP, Azure
- Containerization: Docker, Docker Compose
- 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
- Microservices architecture design
- Data lake and data warehouse design patterns
- ETL/ELT pipeline architecture
- Scalable system design principles
- Infrastructure as Code concepts
- Continuous integration principles
- Monitoring and logging implementations
- Performance optimization strategies
- Machine Learning pipeline integration
- DataOps and MLOps implementations
- Advanced stream processing patterns
Feel free to explore the projects and reach out for discussions on big data engineering, cloud architecture, or distributed systems!
This repository represents my journey through modern big data engineering practices, showcasing hands-on experience with industry-standard tools and real-world project implementations.