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Debezium for Beginners: Master Change Data Capture with Kafka Step-By-Step: Build Real-Time Data Pipelines from Mysql, PostgreSQL & MongoDB to Snowfla
Paperback
Publisher: Independently Published
Published: Oct 30 2025
Pages: 252
Weight: 0.98
Height: 0.53 Width: 7.00 Depth: 10.00
Language: English
Build reliable real time data pipelines from operational databases to Snowflake with production ready Change Data Capture.
Batch jobs miss deadlines, overload primaries, and lose deletes. Teams need low latency feeds that reflect every committed row change with clear keys and schemas.
This book shows how to capture changes with Debezium, stream them through Kafka, and apply them in Snowflake with stable upserts and predictable deletes. You get working defaults, guardrails, and runbooks shaped by real operations.
- set up a full local stack with Kafka, Kafka Connect, Schema Registry, and Debezium UI, then run a MySQL to Kafka to Snowflake smoke test
- operate Kafka Connect the right way, distributed workers, internal topics, baseline worker config, REST and kcctl lifecycle, plugin version pinning and rolling upgrades
- understand the Debezium event model, before and after images, op codes, transaction metadata, and the unwrap transform for stable upserts
- choose converters and subject naming with Avro, JSON Schema, or Protobuf, and enforce compatibility in Schema Registry
- design keys, topic naming, and partitioning that preserve order and make idempotent apply simple
- run MySQL capture with correct binlog settings, grants, TLS, snapshot strategy, and delete handling with tombstones
- run PostgreSQL capture with logical replication, publications, slot management, WAL hygiene, and REPLICA IDENTITY choices for tables without keys
- run MongoDB capture with change streams, update lookup tradeoffs, partial updates, and a clean outbox pattern
- use SMTs, ExtractNewRecordState, delete handling modes, header enrichment, and a robust outbox table design with event routing
- perform initial, initial only, when needed, and schema only snapshots, then backfill safely with signal based incremental snapshots without downtime
- achieve source side exactly once with worker and connector settings, protect offsets and schema history, back up and recover without forced resnapshots
- choose Snowpipe Streaming or Snowpipe and tune for cost and latency, configure the Snowflake Kafka Sink with buffers, channels, and DLQ
- apply changes in Snowflake using Streams and Tasks with MERGE for upserts and deletes, and model SCD1 or SCD2 with Dynamic Tables
- deploy at scale on Kubernetes with Strimzi for Kafka and Connect, Debezium Operator and Debezium Server, and govern access with RBAC and safety rails in Debezium UI
- measure what matters, SLOs for commit to Snowflake freshness, consumer lag, availability, and concrete Prometheus alert queries
- tune performance with snapshot threads, queue and batch sizes, producer overrides, and sink buffers that keep pipelines smooth
- secure every hop with TLS, least privilege database roles, Kafka ACLs, Snowflake key pair auth, and safe secret handling
- run proven drills, schema history loss recovery, PostgreSQL slot bloat triage, and fixing tombstone stalls without guesswork
This is a code heavy guide. Every chapter includes working configs and runnable snippets in YAML, JSON, SQL, Shell, and Systemd Unit so you can build and ship real pipelines.
Grab your copy today and ship a CDC platform that stays fast and quiet in production.
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