37 posts tagged with "Disaster Recovery"

Kafka Streams disaster recovery means recovering state stores, not just topics. Every Streams task keeps its aggregation, join, or window state on local disk — Kafka's replication never touches that disk. The only durable copy of that state Kafka manages is the changelog topic behind it, and that is the thing your recovery plan actually needs to protect.

This post explains why a Streams app needs a different recovery plan than a plain consumer, compares the three ways to get its state back, and works out what state-store size does to your recovery time.

The Kafka MirrorMaker architecture is three Kafka Connect connectors run by one or more worker processes, coordinating cross-cluster replication through two distinct layers of internal Kafka topics. MirrorMaker 2 ships no bespoke server of its own — it inherits everything about how it runs from the Connect framework underneath it.

That inheritance explains most of what confuses people operating MirrorMaker 2 in production: why it needs a Connect cluster at all, what its internal topics actually are, how one config file scales past two clusters, and what changed when exactly-once support arrived.

Kafka point-in-time recovery (PITR) restores topic data to a specific moment — down to the millisecond — by filtering backed-up records on the timestamps they already carry. Kafka itself has no rewind button. Retention deletes forward, and replication copies corruption as faithfully as it copies good data. PITR therefore needs two things the platform does not provide: an immutable copy of the data outside the cluster, and a restore engine that filters that copy by time.

This post explains the mechanism end to end: why native Kafka features cannot rewind topic state, how timestamp filtering works, what happens to consumer groups, and the caveats — clock skew, transactions, compaction — that decide whether the restore you get matches the restore you planned.

Kafka backup compliance comes down to four obligations that appear, in different words, in almost every regulation: retain topic data for a defined period, restore it on demand, prove both with tamper-evident records, and erase personal data when the law requires it. No regulation names Kafka. Auditors simply treat the events in your topics as regulated records — and broker retention, replication factor, and disk durability satisfy none of the four obligations.

Most Kafka estates that fail an audit fail on process, not technology. Retention was set by disk budget instead of regulation, no copy exists outside the cluster, and nobody can produce evidence that a restore has ever worked. This guide maps GDPR, SOX, HIPAA, and PCI DSS to the specific backup controls that close those gaps.

Kafka backup cost comes down to one dominant term — stored gigabytes multiplied by how long you keep them — plus smaller charges for requests, data transfer, and the compute that runs the backup job. Three levers cut that dominant term, and they multiply: compression shrinks every byte you store, incremental backups stop you from re-storing bytes you already have, and storage-class lifecycle rules move aged bytes to tiers that cost up to 23x less.

A naive setup — uncompressed daily fulls parked in S3 Standard — routinely costs 10 to 50 times more than an optimized pipeline holding the same data with better recovery guarantees. This guide breaks down where the money goes, walks each lever with real configuration, and prices a worked example at the end.

Kafka multi region replication is not one decision — it is three layers. A multi-region Kafka architecture combines replica placement across availability zones inside a single cluster, asynchronous replication or replicated backups across regions, and an independent backup that can rewind past the failures every replica copies faithfully. Which layers you build depends on which failure domains you must survive, and at what recovery point and recovery time.

This guide climbs the failure-domain ladder from a dead broker to a lost region to a bad deploy, shows what each layer contributes with real configuration, and closes with a decision table that maps recovery objectives to the cheapest architecture that meets them.

Kafka to Kafka replication continuously copies topics from one cluster to another using a replication tool. MirrorMaker 2 ships with Apache Kafka and is the default choice; Confluent, AWS, and others sell managed or commercial alternatives that trade money for operations.

The tool matters less than matching the mechanism to the job. Disaster recovery, cluster migration, geo distribution, aggregation, and environment seeding each pull toward different choices. And one boundary holds for all of them: replication keeps a second cluster near the present — it cannot give you yesterday back.

The Kafka Replicator vs MirrorMaker decision comes down to ownership. MirrorMaker 2 is the replication framework included with Apache Kafka: free, vendor-neutral, and built on Kafka Connect. Kafka Replicator — almost always Confluent Replicator — is Confluent's commercial connector with deeper Confluent Platform integration, including Schema Registry migration and Control Center monitoring.

The feature lists overlap heavily. The real differences sit in licensing, destination topic naming, consumer offset translation, and schema handling. Neither tool can restore yesterday's data, so neither replaces backup.

Strimzi Kafka backup is the piece Strimzi deliberately leaves to you. Strimzi manages the lifecycle of Kafka on Kubernetes — brokers, topics, users, certificates — but it does not back up the message data inside your topics. Re-applying your Kafka and KafkaTopic resources rebuilds an empty cluster, not your data.

This guide maps what Strimzi protects and what it does not, explains why volume snapshots are not a substitute, and walks through the declarative alternative: a Strimzi-native operator that runs backups and restores as custom resources next to your Kafka CR.

The Kafka Connect S3 sink connector consumes Kafka topics and writes their records to Amazon S3 as partitioned object files in Avro, JSON, Parquet, or raw bytes. If you already run a Kafka Connect cluster, it is the standard way to archive topic data for analytics, long-term retention, and compliance records.

This guide covers how the connector works, a complete working configuration, the partitioners and formats that shape your archive, the exact conditions for exactly-once delivery, licensing, and the one boundary every team should plan around: an S3 sink archive has no first-class path back into Kafka.