Welcome to my Databricks and Cloud Proficiency Showcase! This repository highlights my expertise in Databricks, Azure Data Factory, KeyVault, ADLS GEN2, and Synapse. Dive into my comprehensive knowledge of these tools and explore my work in creating robust data pipelines, managing data with the Medallion architecture, and leveraging Databricks features for advanced data engineering tasks.

1. Introduction
2. Azure Data Factory
3. Azure KeyVault
4. Azure Data Lake Storage Gen2
5. Azure Synapse
6. Databricks
   • Databricks Features
     • Delta Live Tables (DLT)
     • Change Data Capture (CDC)
     • Medallion Architecture
     • Workflow Jobs and Orchestration
7. Resources

In the modern data landscape, mastering cloud-based data engineering tools is crucial. This guide showcases my proficiency in leveraging Databricks and related Azure services to build and manage scalable, reliable data solutions.

• Data Integration: Orchestrated complex ETL workflows using Azure Data Factory, ensuring seamless data movement and transformation across various data sources.
• Pipeline Automation: Automated data pipelines to enhance efficiency and reduce manual intervention, ensuring timely data availability for downstream processes.
• Scheduling and Triggering: Implemented sophisticated scheduling and triggering mechanisms to optimize data processing and resource utilization.

• Secret Management: Secured sensitive data and managed cryptographic keys using Azure KeyVault, ensuring robust security for cloud applications.
• Access Control: Configured fine-grained access controls to protect secrets and keys, adhering to best practices for cloud security.

• Big Data Analytics: Leveraged ADLS Gen2 capabilities to store and manage large-scale data for analytics, ensuring high performance and scalability.
• Hierarchical Namespace: Utilized the hierarchical namespace feature to organize data efficiently and improve data management practices.
• Data Security: Implemented advanced security measures, including encryption and access controls, to safeguard data stored in ADLS Gen2.

• Integrated Analytics: Combined data warehousing and big data analytics in Azure Synapse, enabling comprehensive data insights and faster decision-making.
• SQL Pools: Created and managed dedicated SQL pools for high-performance data processing and querying, supporting large-scale analytics workloads.
• End-to-End Solutions: Developed end-to-end analytics solutions, integrating Synapse with other Azure services to deliver seamless data workflows.

• ETL Pipeline Management: Utilized DLT to simplify the creation and management of ETL pipelines, ensuring data reliability and reducing operational overhead.
• Declarative Pipelines: Implemented declarative pipelines for automated data transformations, enhancing maintainability and readability.

• Incremental Data Processing: Employed CDC to efficiently capture and process data changes, ensuring up-to-date data for analytics and reporting.
• Data Lake Integration: Integrated CDC with data lake storage to maintain a consistent and current dataset, supporting real-time data analysis.

• Layered Data Organization: Designed and implemented the Medallion architecture, organizing data into bronze, silver, and gold layers to streamline data processing and analytics.
• Scalable Data Pipelines: Constructed scalable data pipelines to manage data flows across different layers, ensuring data quality and consistency.

• Complex Workflows: Orchestrated complex workflows using Databricks Jobs, automating the execution of notebooks and ensuring efficient task management.
• Cross-Service Integration: Integrated Databricks with other Azure services to create cohesive data workflows, enhancing overall system efficiency and reliability.

1. Azure Data Factory Documentation
2. Azure KeyVault Documentation
3. Azure Data Lake Storage Gen2 Documentation
4. Azure Synapse Documentation
5. Databricks Documentation
6. Delta Lake Documentation

This showcase provides an in-depth look at my expertise in utilizing these powerful tools and features to deliver advanced data engineering solutions in the cloud. Explore the accompanying Databricks notebooks to see my work in action, demonstrating my proficiency in building robust and scalable data pipelines.