37 posts tagged with "Disaster Recovery"

Choosing the right Kafka backup tool is the difference between a five-minute recovery and a five-hour scramble. There is no single winner. Each tool solves a specific problem: MirrorMaker 2 and MSK Replicator keep a warm cluster for regional failover, Kafka Connect S3 Sink archives topics to cheap object storage, and point-in-time backup restores a topic to the moment before a bad deploy. This guide compares all four, shows where each fits, and gives you a decision matrix so you can match tools to requirements instead of the other way around.

MSK Replicator is a fully managed feature of Amazon MSK that copies topic data, consumer group offsets, and topic configurations between MSK clusters — across regions or across accounts — without you running any replication infrastructure. AWS manages the brokers, but cross-region disaster recovery for your streams is still your responsibility. MSK Replicator closes that gap. This guide covers its architecture, how to set it up for cross-region DR, failover procedures, and when to choose it over MirrorMaker 2.

Kafka disaster recovery is the practice of keeping a second, independent copy of your streaming data and cluster metadata so you can resume service after a region outage, a bad deploy, or human error. Kafka's built-in replication — replication factor 3 with min.insync.replicas=2 — survives broker failure, but it does not survive a lost region or a deleted topic. The right DR design depends on three numbers: your recovery time objective (RTO), your recovery point objective (RPO), and your budget. This guide compares active-passive and active-active architectures and gives you a framework to choose.

Kafka backup means copying topic data, consumer group offsets, and cluster metadata to independent storage — object storage or a filesystem — so you can restore it after an incident. Replication alone cannot do this: it faithfully copies deletions and corrupt records to every replica in real time. A complete Kafka backup strategy combines replication for hardware resilience with periodic or continuous backups for protection against logical errors, regional outages, and compliance obligations.

Kafka backup best practices come down to one principle: a backup you have not restored is a hope, not a backup. Retention deletes your data on schedule, replication copies your mistakes in real time, and neither can return a topic to the state it was in before an incident. These 10 rules turn Kafka backups from a checkbox into something you can bet an on-call shift on.

Kafka geo replication copies topics between clusters in different regions or datacenters, so a regional outage does not take your streaming platform with it. In-cluster replication (RF=3) protects against broker loss inside one failure domain; geo replication protects against losing the domain itself. This guide compares the four patterns, shows what MirrorMaker 2 setup looks like over a WAN, and covers the failure mode replication cannot solve.

Kafka replication across data centers comes down to one architectural decision: run a single stretched cluster spanning your datacenters, or run independent clusters connected by asynchronous replication. Stretched clusters give you zero data loss but demand low-latency links. Separate clusters tolerate WAN conditions but accept a non-zero recovery point. This guide covers both designs, when each wins, and how to build the second one with MirrorMaker 2.