Learn how to design scalable, secure, and cost-aware data ingestion pipelines for batch and real-time streaming workloads. This course is designed for data engineers and learners interested in modern data ingestion architecture and reliable data processing.

You'll compare batch and real-time ingestion models, assess latency and processing requirements, and explore Lambda and Kappa architectures. Examine streaming engines, Parquet, Avro, and schema evolution to understand the trade-offs behind effective pipeline design. Discover how security, performance tuning, backpressure, windowing, and stream joins support dependable streaming pipelines. You'll also explore pipeline observability, compliance, and ingestion strategy, alongside Apache Beam for portable batch and streaming workflows. Evaluate how edge ingestion, 5G, and FinOps shape decisions about performance, portability, and cloud costs. By the end of the course, you'll be able to evaluate ingestion requirements, select suitable architectural patterns, and assess ways to improve pipeline reliability, governance, and cost efficiency. What sets this course apart is its integrated view of batch and streaming architecture, from foundational design choices to observability, portability, and emerging ingestion needs—helping you make informed data engineering decisions.