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bongoDev | Change Data Capture (CDC): Reliability Between Database Log and Event Stream - Event-Driven Reliability Pro

TopicEvent-Driven Reliability Pro

01Event-Driven Reliability Mental Model: Publish, Persist, Deliver, Process, AcknowledgePreview
02Synchronous vs Asynchronous Architecture: Choose Failure Semantics Deliberately
03Event, Message, Command, Notification & Task: Model Intent Correctly
04Queues, Topics, Streams & Logs: Pick the Right Delivery Shape
05Producer → Broker → Consumer: Responsibility Handoffs & Failure Windows
06Delivery Semantics: At-Most-Once, At-Least-Once & Exactly-Once Boundaries
07Message Envelope Design: IDs, Correlation, Causation, Time, Source & Version
08Event Naming & Domain Semantics: Facts Should Survive Implementation Changes
09Schema Evolution: Backward, Forward & Full Compatibility
10Schema Registry & Contract Governance
11Event Time vs Processing Time: Reason About Late and Delayed Events
12Ordering Guarantees: Define the Scope Before Depending on Order
13Partitioning & Sharding: Scale Without Losing Per-Key Correctness
14Hot Partitions, Hot Keys & Skew
15Retention, Replay & Event History
16Consumer Groups & Competing Consumers
17Acknowledgements, Visibility Timeouts & Processing Leases
18Acknowledge After the Durable Side Effect
19Producer Confirms & Publish Durability
20Ambiguous Publish Outcomes & Producer Deduplication
21Idempotent Consumers: Make Redelivery Safe
22Deduplication Stores, Unique Constraints & Retention Windows
23Transactional Outbox: Database State and Event Publication as One Intent
24Inbox Pattern: Atomic Consumer Deduplication & State Change
25Change Data Capture (CDC): Reliability Between Database Log and Event Stream
26Saga Pattern: Distributed Business Transactions Without a Global Lock
27Orchestration vs Choreography
28Exactly-Once Processing: Define the Transactional Boundary
29Retry Classification: Transient, Persistent & Business Failures
30Exponential Backoff & Jitter for Consumers
31Retry Topics / Retry Queues: Keep Failure Delays Out of Hot Consumers
32Poison Messages: Detect Deterministic Consumer Failure
33Dead-Letter Queues: Quarantine, Not a Graveyard
34Safe DLQ Redrive: Repair Before Replay
35Message TTL, Expiration & Delayed Work
36Backpressure: Let Downstream Capacity Control Upstream Work
37Prefetch / Max In-Flight: Throughput vs Fairness vs Recovery
38Queue Depth vs Consumer Lag vs Oldest Message Age
39Drain Time & Recovery Capacity
40Event-Driven Autoscaling: Scale on Work, Not Only CPU
41Overload Protection: Admission Control, Quotas & Load Shedding
42Head-of-Line Blocking
43Slow Consumers & Consumer Starvation
44Consumer Rebalances, Ownership Changes & Duplicate Windows
45Request/Reply over Messaging: Correlation, Timeouts & Orphans
46Publish/Subscribe Fan-Out & Independent Consumer Failure
47Work Queue / Competing Consumer Pattern
48Event Log / Stream Pattern: Replayable State Changes
49Kafka Reliability Integration: What This Track Adds Beyond Kafka Pro
50RabbitMQ Reliability: Publisher Confirms, Manual Acks, Quorum Queues & DLX
51Amazon SQS Reliability: Visibility, Long Polling, DLQ & FIFO Message Groups
52Google Cloud Pub/Sub Reliability: Ack Deadlines, Ordering & Exactly-Once Scope
53Azure Service Bus Reliability: Peek-Lock, Sessions, DLQ & Duplicate Detection
54NATS JetStream Reliability: Streams, Consumers, Acks & Redelivery
55Choose the Messaging Platform by Semantics, Not Popularity
56Multi-Tenancy, Quotas & Noisy-Neighbor Isolation
57Authentication, Authorization, TLS & Least Privilege
58Sensitive Data, PII & Event Payload Minimization
59Auditability & Event Lineage
60Event Observability: Producer, Broker, Consumer & Business Signals
61Distributed Tracing Across Asynchronous Boundaries
62OpenTelemetry Messaging Spans, Links & Async Causality
63Structured Logging & Correlation for Event Pipelines
64SLOs for Asynchronous Systems: Freshness, Completion & Loss
65Alerting on Backlog: Page on Impact, Not Every Queue
66Capacity Planning for Event Pipelines
67Backlog Storage, Retention & Disk Risk
68High Availability & Broker Failure Domains
69Multi-Region Event Architecture: Active/Passive, Active/Active & Conflict Risk
70Disaster Recovery for Messaging: Restore the Pipeline, Not Just the Broker
71Event Replay Safety: Rebuild Derived State Without Repeating External Effects
72Backfills & Historical Reprocessing
73Stateful Stream Processing: Checkpoints, State Stores & Recovery
74Windowing, Watermarks & Late Events
75Release Engineering for Producers & Consumers
76Consumer Version Skew & Rolling Deployments
77Configuration & Secret Changes in Event Systems
78Testing Event-Driven Systems: Contracts, Integration, Replay & Failure
79Deterministic Consumer Tests with Duplicate & Out-of-Order Events
80Load & Soak Testing Event Pipelines
81Chaos & Resilience Testing for Messaging
82Event-Driven Incident Patterns: Lost, Duplicated, Delayed, Reordered & Stuck
83Incident: Queue Backlog & Consumer Lag
84Incident: Duplicate Business Side Effects
85Incident: Poison Message & Retry Storm
86Incident: Broker Healthy, Business Pipeline Broken
87Lab: Reliable Order Pipeline with Outbox, Idempotent Consumer & DLQ
88Lab: Retry Storm, Backpressure & Controlled Recovery
89Lab: Poison Message, DLQ Repair & Canary Redrive
90Lab: Consumer Lag, Autoscaling & Downstream Capacity Guardrail
91Lab: Kafka/RabbitMQ/SQS Semantics Comparison
92Lab: Regional Failure & Replay-Based Recovery
93Event-Driven Reliability Troubleshooting Playbook
94Event-Driven Command & Evidence Playbook
95Event-Driven Reliability Pro Cheatsheet

Englishবাংলা

Knowledge Hub/Event-Driven Reliability Pro/Change Data Capture (CDC): Reliability Between Database Log and Event Stream

Event-Driven Reliability Pro

Paid lesson · Advanced · 42 min read

Change Data Capture (CDC): Reliability Between Database Log and Event Stream

Understand CDC offset/checkpoint, schema and replay failure modes when database changes become event streams.

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PreviousInbox Pattern: Atomic Consumer Deduplication & State ChangeLocked NextSaga Pattern: Distributed Business Transactions Without a Global LockLocked

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