Artwork

Inhalt bereitgestellt von Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka®. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka® oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.
Player FM - Podcast-App
Gehen Sie mit der App Player FM offline!

Migrate Your Kafka Cluster with Minimal Downtime

1:01:30
 
Teilen
 

Manage episode 424666708 series 2510642
Inhalt bereitgestellt von Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka®. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka® oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.

Migrating Apache Kafka® clusters can be challenging, especially when moving large amounts of data while minimizing downtime. Michael Dunn (Solutions Architect, Confluent) has worked in the data space for many years, designing and managing systems to support high-volume applications. He has helped many organizations strategize, design, and implement successful Kafka cluster migrations between different environments. In this episode, Michael shares some tips about Kafka cluster migration with Kris, including the pros and cons of the different tools he recommends.
Michael explains that there are many reasons why companies migrate their Kafka clusters. For example, they may want to modernize their platforms, move to a self-hosted cloud server, or consolidate clusters. He tells Kris that creating a plan and selecting the right tool before getting started is critical for reducing downtime and minimizing migration risks.
The good news is that a few tools can facilitate moving large amounts of data, topics, schemas, applications, connectors, and everything else from one Apache Kafka cluster to another.
Kafka MirrorMaker/MirrorMaker2 (MM2) is a stand-alone tool for copying data between two Kafka clusters. It uses source and sink connectors to replicate topics from a source cluster into the destination cluster.
Confluent Replicator allows you to replicate data from one Kafka cluster to another. Replicator is similar to MM2, but the difference is that it’s been battle-tested.
Cluster Linking is a powerful tool offered by Confluent that allows you to mirror topics from an Apache Kafka 2.4/Confluent Platform 5.4 source cluster to a Confluent Platform 7+ cluster in a read-only state, and is available as a fully-managed service in Confluent Cloud.
At the end of the day, Michael stresses that coupled with a well-thought-out strategy and the right tool, Kafka cluster migration can be relatively painless. Following his advice, you should be able to keep your system healthy and stable before and after the migration is complete.
EPISODE LINKS

  continue reading

Kapitel

1. Intro (00:00:00)

2. Why would you migrate a Kafka cluster? (00:01:46)

3. Is it easy to migrate a Kafka cluster? (00:05:06)

4. MirrorMaker and MirrorMaker 2 (00:06:38)

5. Confluent Replicator (00:15:04)

6. Cluster Linking (00:18:39)

7. Getting started with Kafka cluster migration (00:22:51)

8. Can you gradually migrate your Kafka cluster? (00:29:47)

9. Is there a recommended strategy for migrating your Kafka cluster? (00:33:10)

10. Do you have to migrate all your consumer groups at once? (00:36:14)

11. Migrating Kafka Streams applications (00:38:29)

12. Migrating connectors (00:44:32)

13. Migrating schemas (00:48:12)

14. How long does it take to do a Kafka cluster migration? (00:54:44)

15. What is the biggest challenge of Kafka cluster migration? (00:57:45)

16. It's a wrap! (00:59:38)

265 Episoden

Artwork
iconTeilen
 
Manage episode 424666708 series 2510642
Inhalt bereitgestellt von Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka®. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka® oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.

Migrating Apache Kafka® clusters can be challenging, especially when moving large amounts of data while minimizing downtime. Michael Dunn (Solutions Architect, Confluent) has worked in the data space for many years, designing and managing systems to support high-volume applications. He has helped many organizations strategize, design, and implement successful Kafka cluster migrations between different environments. In this episode, Michael shares some tips about Kafka cluster migration with Kris, including the pros and cons of the different tools he recommends.
Michael explains that there are many reasons why companies migrate their Kafka clusters. For example, they may want to modernize their platforms, move to a self-hosted cloud server, or consolidate clusters. He tells Kris that creating a plan and selecting the right tool before getting started is critical for reducing downtime and minimizing migration risks.
The good news is that a few tools can facilitate moving large amounts of data, topics, schemas, applications, connectors, and everything else from one Apache Kafka cluster to another.
Kafka MirrorMaker/MirrorMaker2 (MM2) is a stand-alone tool for copying data between two Kafka clusters. It uses source and sink connectors to replicate topics from a source cluster into the destination cluster.
Confluent Replicator allows you to replicate data from one Kafka cluster to another. Replicator is similar to MM2, but the difference is that it’s been battle-tested.
Cluster Linking is a powerful tool offered by Confluent that allows you to mirror topics from an Apache Kafka 2.4/Confluent Platform 5.4 source cluster to a Confluent Platform 7+ cluster in a read-only state, and is available as a fully-managed service in Confluent Cloud.
At the end of the day, Michael stresses that coupled with a well-thought-out strategy and the right tool, Kafka cluster migration can be relatively painless. Following his advice, you should be able to keep your system healthy and stable before and after the migration is complete.
EPISODE LINKS

  continue reading

Kapitel

1. Intro (00:00:00)

2. Why would you migrate a Kafka cluster? (00:01:46)

3. Is it easy to migrate a Kafka cluster? (00:05:06)

4. MirrorMaker and MirrorMaker 2 (00:06:38)

5. Confluent Replicator (00:15:04)

6. Cluster Linking (00:18:39)

7. Getting started with Kafka cluster migration (00:22:51)

8. Can you gradually migrate your Kafka cluster? (00:29:47)

9. Is there a recommended strategy for migrating your Kafka cluster? (00:33:10)

10. Do you have to migrate all your consumer groups at once? (00:36:14)

11. Migrating Kafka Streams applications (00:38:29)

12. Migrating connectors (00:44:32)

13. Migrating schemas (00:48:12)

14. How long does it take to do a Kafka cluster migration? (00:54:44)

15. What is the biggest challenge of Kafka cluster migration? (00:57:45)

16. It's a wrap! (00:59:38)

265 Episoden

Tất cả các tập

×
 
Loading …

Willkommen auf Player FM!

Player FM scannt gerade das Web nach Podcasts mit hoher Qualität, die du genießen kannst. Es ist die beste Podcast-App und funktioniert auf Android, iPhone und im Web. Melde dich an, um Abos geräteübergreifend zu synchronisieren.

 

Kurzanleitung