Build modern data engineering skills across ingestion, transformation, modeling, distributed processing, lakehouse architecture, and cloud storage. Learn how to design scalable, reliable, governed, and cost-aware data platforms for analytics and machine learning. This Specialization develops an integrated understanding of the modern data engineering lifecycle. You’ll evaluate batch and streaming ingestion architectures, design analytical data models, compare ETL and ELT approaches, and explore transformation patterns including CDC, temporal modeling, semantic layers, and feature stores.
You’ll examine distributed processing with Apache Spark, including partitioning, shuffles, data skew, Adaptive Query Execution, and resource optimization. You’ll also explore lakehouse architecture, Delta Lake, Apache Iceberg, Apache Hudi, Parquet, Avro, cloud storage, caching, and storage tiering. Across the Specialization, you’ll consider data quality, governance, security, lineage, observability, performance, FinOps, and scalability. By completion, you’ll be able to evaluate architectural requirements, compare modern data technologies, optimize data workloads, and make informed decisions for reliable analytics and machine learning platforms.
