Manage Schema Evolution in Real‑Time Data

Overview

Ship data and schema changes without outages. This hands-on course teaches you how to treat schemas as contracts, evolve them safely, and keep producers, consumers, and warehouses green end-to-end. You’ll design compatibility policies in a Schema Registry (backward/forward/full, transitive), automate checks in CI, and practice expand → adapt → contract rollouts. In streaming labs, you’ll capture OLTP changes with Debezium, deliver Avro-encoded events to Kafka, and route malformed records to a DLQ with actionable alerts. On the analytics side, you’ll evolve BigQuery/Iceberg schemas additively (NULLABLE/defaulted columns), shield downstream users with views/contracts, and validate correctness with queries and time travel. Realistic scenarios walk you through enum expansions, type widening, null/tombstone semantics, and subject naming rules. This course is for data engineers, backend engineers, and analytics engineers who work with real-time or streaming data systems and need to evolve schemas without downtime. It’s also useful for platform engineers and architects responsible for data contracts, CDC pipelines, or Kafka-based platforms. Learners should have basic SQL knowledge and a general understanding of streaming systems such as Kafka, along with familiarity with Git and the command line. Experience with schemas, CDC, Docker, or cloud data warehouses is helpful but not required. By the end, you’ll have runnable templates, governance checklists, and a portfolio-ready project that proves you can design zero-downtime change—confidently and repeatably. For more information, check out the document.

Syllabus

Principles & Patterns of Real‑Time Schema Evolution

Establish the why/what/how of schema evolution in streaming systems. Cover compatibility modes, additive vs. breaking changes, deprecation, and common anti‑patterns.

Implementing Contracts with Registries & CI/CD (Condensed Reading)

Learners review registry basics, CI checks, rollout playbooks, and observability as self‑study materials. No in‑session time is allocated in the 60‑minute run.

Zero-Downtime Data Changes: CDC, Compatibility, and Rollouts

This module teaches you how to ship schema and data-model changes without outages using CDC pipelines, registries, and guardrails. You’ll learn rollout patterns (expand → adapt → contract), handle nulls/tombstones in Debezium, and make warehouses (e.g., BigQuery) resilient with nullable fields and views. By the end, you can design end-to-end plans with DLQs, alerts, and rollback levers that keep producers, consumers, and analytics running smoothly.