What you'll learn

Modern data platforms depend on reliable ingestion pipelines to power analytics, reporting, and machine learning. Understanding how data moves from source systems into lakes, warehouses, and analytical platforms is critical for building scalable and trustworthy data solutions.

In this course, Big Data Foundations: Data Ingestion and Movement Patterns, you’ll learn how modern big data systems ingest data using batch, micro-batch, and real-time streaming approaches.

First, you’ll explore the core principles and challenges of ingesting data at scale, including throughput, ordering, late data, schema drift, and delivery guarantees.

Next, you’ll examine the major categories of ingestion tools and services, such as distributed event logs, ETL and ELT pipelines, change data capture, and managed cloud ingestion services.

Finally, you’ll learn the architectural patterns used to move and synchronize data across systems, including replication, event-driven propagation, orchestration workflows, and integration patterns across lakes, warehouses, and operational systems.

By the end of this course, you’ll be able to reason about ingestion choices, explain trade-offs, and understand how modern data platforms move data reliably at scale.