Master Data Engineering with Azure: From Fundamentals to Real-World Projects in Spark, SQL, and Databricks
What you will learn
Fundamentals of Data Engineering: Understand the core concepts, roles, and responsibilities within data engineering, including data lifecycle management.
SQL Proficiency: Master both basic and advanced SQL techniques for querying, data modeling, and optimizing database performance.
Python Programming: Gain hands-on experience in Python, focusing on essential programming concepts, data manipulation, and file handling.
Databricks & PySpark Skills: Learn to use Databricks for data processing and transformations with PySpark, including building efficient ETL pipelines.
Azure Services Expertise: Explore various Azure services, including Azure Data Factory, Azure Synapse, and Azure Storage, for data integration and analytics.
Data Visualization with Power BI: Create interactive dashboards and reports using Power BI, integrating data from multiple sources and leveraging AI tools.
Real-World Project Experience: Apply learned skills in practical projects that simulate industry scenarios, enhancing problem-solving and project management abi
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- Architecting Scalable Data Platforms: Design, deploy, and manage robust, highly available, and fault-tolerant data ecosystems on Azure. Strategically integrate diverse services to build production-ready infrastructures for enterprise demands.
- Advanced Performance Engineering: Master optimizing data storage, processing, and query execution across Azure services. Ensure cost-efficiency and rapid data delivery with petabytes of information under extreme loads.
- Robust Data Governance & Security: Implement critical data security, privacy, and compliance measures within Azure. Apply advanced access controls, encryption, and data masking strategies for regulatory adherence and data integrity.
- Automated DataOps & CI/CD: Embrace DataOps by automating the full data lifecycle. Build CI/CD pipelines, integrate automated testing, and establish proactive monitoring for continuous reliability and infrastructure improvement.
- Real-time & Intelligent Data Pipelines: Design pipelines for real-time stream analytics, integrating diverse data sources (structured, semi-structured, unstructured) into unified platforms. Prepare data for advanced AI/ML workloads efficiently.
- Strategic Insights & Data Storytelling: Translate complex data into clear, actionable business insights. Craft compelling narratives that drive strategic decisions, predict trends, and uncover opportunities, elevating beyond basic reporting.
- Operational Excellence & Monitoring: Gain expertise in deploying, managing, and monitoring production-grade Azure data solutions. Implement best practices for logging, alerting, disaster recovery, and ensuring operational excellence.
- PROS:
- Holistic Skill Mastery: Develop comprehensive data engineering skills from foundational coding to advanced cloud architecture and strategic data visualization, highly valued industry-wide.
- Azure Cloud Specialization: Become an expert in Azure, a leading cloud platform, significantly enhancing your employability and future-proofing your career.
- Accelerated Career Path: This intensive, project-based “Masters” approach profoundly boosts your profile, preparing you for senior data engineering, architect, and leadership roles.
- CONS:
- Intensive Learning Pace: The program’s comprehensive, rapid coverage of numerous advanced technologies demands substantial dedication, potentially posing a steep learning curve for those new to programming or core data concepts.
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