Overview

This comprehensive specialization equips data professionals with the skills to design, build, and optimize modern cloud data platforms that combine the flexibility of data lakes with the reliability of data warehouses. Through 13 hands-on courses, you'll learn to provision secure cloud infrastructure using Infrastructure as Code, implement lakehouse architectures with transactional integrity, build automated data pipelines using Spark, dbt, and Airflow, and optimize performance across storage and query layers. You'll also develop expertise in data security, compliance frameworks, disaster recovery planning, and enterprise-grade data reconciliation—emerging with the complete skillset to architect production-ready data systems that deliver measurable ROI.

Syllabus

Course 1: Engineer Cloud Data for Resiliency & ROI
Course 2: Transform, Analyze, and Optimize Your Data
Course 3: Secure Data: Mask, Monitor, and Audit
Course 4: Provision Secure Cloud Data Infrastructure
Course 5: Apply Data Lake Transactions & Versioning
Course 6: Build & Transform Data Pipelines
Course 7: Map Data Flows Fast
Course 8: Optimize Spark Performance: Analyze & Accelerate

Courses

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2 hours 27 minutes
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Did you know that 96% of organizations experience unplanned downtime, costing an average of $5,600 per minute? This critical reality makes engineering resilient cloud data infrastructure not just a best practice—it's a business imperative. This Short Course was created to help data engineers and platform architects accomplish the mission-critical task of building cloud data warehouses that deliver both optimal ROI and bulletproof reliability. By completing this course, you'll be able to automate infrastructure provisioning with code-based deployment systems, make data-driven decisions on compute and storage configurations that maximize cost-effectiveness, and architect disaster recovery systems that protect against catastrophic failures with minimal data loss. By the end of this course, you will be able to: - Apply Infrastructure as Code (IaC) to provision a cloud data warehouse - Analyze infrastructure cost versus performance across compute and storage options - Create a cross-region disaster recovery architecture with a 15-minute Recovery Point Objective This course is unique because it combines hands-on Terraform automation with real-world TPC-DS benchmarking and enterprise-grade disaster recovery planning—skills that directly translate to building production-ready data platforms. To be successful in this project, you should have a background in SQL, basic cloud computing concepts, and familiarity with data warehouse fundamentals.
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1 hour 22 minutes
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Unlock the performance potential of your Apache Spark applications! This course transforms beginners into confident Spark performance optimizers who can dramatically improve job execution times and resource efficiency. This course is a direct response to industry demand, designed for the data engineer who is tired of reactive firefighting and ready to build proactively optimized, scalable systems. This Short Course was created to help data management and engineering professionals accomplish systematic Spark job optimization through strategic analysis of partitioning and caching patterns. By completing this course, you'll be able to inspect query execution plans in Spark UI, implement strategic partitioning keys that minimize data shuffling, persist intermediate DataFrames with appropriate storage levels, and validate performance improvements that you can apply immediately in your workplace. By the end of this course, you will be able to: Analyze partitioning and caching strategies to optimize Spark job performance This course is unique because it combines hands-on analysis using real Spark UI inspection with practical implementation techniques that deliver measurable performance gains – often 30% or more runtime improvements. To be successful in this project, you should have a background in basic Apache Spark concepts and data processing fundamentals.