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.
Applied Learning Project
Through practical, scenario-based projects, learners will design batch and streaming ingestion pipelines, develop analytics-ready data models, evaluate ETL and ELT transformations, and optimize distributed processing workloads. They will apply architectural decision-making skills to compare lakehouse technologies, select efficient storage formats, and address real-world challenges involving scalability, data quality, governance, performance, reliability, and cloud cost optimization.
