# Using Multiple Connectors VS Multiple Topics on Single Connector

**URL:** <https://forum.confluent.io/t/using-multiple-connectors-vs-multiple-topics-on-single-connector/10449>\
**Category:** Kafka Connect\
**Created:** [25 March 2024 07:05 UTC](https://forum.confluent.io/t/using-multiple-connectors-vs-multiple-topics-on-single-connector/10449 "2024-03-25T07:05:24Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![yonatandt](https://avatars.discourse-cdn.com/v4/letter/y/779978/32.png) [@yonatandt](https://forum.confluent.io/u/yonatandt)\
**Post date:** [25 March 2024 07:05 UTC](https://forum.confluent.io/t/using-multiple-connectors-vs-multiple-topics-on-single-connector/10449/1 "2024-03-25T07:05:25Z")

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I have two Kafka topics (`topic_A` and `topic_B`) with identical partitioning (4 partitions) and similar data. Both topics require the same processing work.

Currently, I’m considering a single Kafka Connect Sink connector to write data to BigQuery, with `tasks.max` set to 8. This setup will run within a Kubernetes (K8s) cluster configured to scale up pods/workers based on topic latency, pod CPU, or pod memory usage.

Are there advantages to using two separate Sink connectors (one per topic) instead of a single connector for this scenario? If so, what are they? I’m primarily interested in factors like performance, fault tolerance, scaling flexibility, and overall management.

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**Author:** ![system](https://us1.discourse-cdn.com/flex019/uploads/confluentcommunity/original/1X/c49438c90c9df282e9996fdf6971be890c71b65a.svg) [@system](https://forum.confluent.io/u/system)\
**Post date:** [24 April 2024 07:05 UTC](https://forum.confluent.io/t/using-multiple-connectors-vs-multiple-topics-on-single-connector/10449/2 "2024-04-24T07:05:49Z")

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