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.
Applied Learning Project
Throughout this specialization, learners complete hands-on projects that simulate real-world data engineering challenges. Projects include provisioning secure cloud data infrastructure with Terraform, building lakehouse architectures with external tables and open-source table formats (Delta Lake, Iceberg, Hudi), designing end-to-end data pipelines using Spark, dbt, and Airflow, implementing data masking and audit logging for compliance, configuring disaster recovery with defined Recovery Point Objectives, and optimizing both Spark jobs and database queries for production performance. Each project reinforces practical skills that translate directly to enterprise data platform development.
