Create Kafka Connectors

Prerequisites

Before deploying a kafka connect to StreamNative Cloud, make sure the following prerequisites have been met:

A running external data system service.
A running Pulsar Cluster with Kop feature enabled on StreamNative Cloud and the required environment has been set up.

Create a built-in kafka connect

Before creating a kafka connect, it’s highly recommended to do the following:

Check kafka connect availability to ensure the version number of the kafka connect you want to create is supported on StreamNative Cloud.
Go to StreamNative Hub and find the connect-specific docs of your version for configuration reference.

You may see below error logs for the first time you create a connector:

org.apache.kafka.common.config.ConfigException: Topic '__kafka_connect_offset_storage' supplied via the 'offset.storage.topic' property is required to have 'cleanup.policy=compact' to guarantee consistency and durability of source connector offsets, but found the topic currently has 'cleanup.policy=delete'. Continuing would likely result in eventually losing source connector offsets and problems restarting this Connect cluster in the future. Change the 'offset.storage.topic' property in the Connect worker configurations to use a topic with 'cleanup.policy=compact'.

You should set the cleanup.policy of the __kafka_connect_offset_storage topic to compact to avoid the above error with below command:

./bin/kafka-configs.sh --bootstrap-server xxxx:9093 --command-config ~/kafka/kafka-token.properties --alter --topic __kafka_connect_offset_storage --add-config cleanup.policy=compact

The following example shows how to create a data generator source connect named test on Streamnative Cloud using different tools.

snctl
kcctl
Rest API
Console

To create a data generator source connect named test, run the following command.

> cat datagen.json
{
  "name": "test",
  "config": {
    "connector.class": "io.confluent.kafka.connect.datagen.DatagenConnector",
    "kafka.topic": "testusers",
    "quickstart": "users",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "max.interval": 1000,
    "iterations": 10000000,
    "tasks.max": "1"
  }
}
> snctl kafka admin connect apply -f datagen.json --use-service-account

You should see the following output:

Created connector test

If you want to verify whether the data generator source connect has been created successfully, run the following command:

snctl kafka admin connect get connectors

You should see the following output:

 NAME                  TYPE     STATE        TASKS
 test                  source   RUNNING      0: RUNNING

To create a data generator source connect named test, run the following command.

> cat datagen.json
{
  "name": "test",
  "config": {
    "connector.class": "io.confluent.kafka.connect.datagen.DatagenConnector",
    "kafka.topic": "testusers",
    "quickstart": "users",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "max.interval": 1000,
    "iterations": 10000000,
    "tasks.max": "1"
  }
}
> kcctl apply -f datagen.json

You should see the following output:

Created connector test

If you want to verify whether the data generator source connect has been created successfully, run the following command:

kcctl get connectors

You should see the following output:

 NAME                  TYPE     STATE        TASKS
 test                  source   RUNNING      0: RUNNING

To create a data generator source connect named test, run the following command.

> cat datagen.json
{
  "name": "test",
  "config": {
    "connector.class": "io.confluent.kafka.connect.datagen.DatagenConnector",
    "kafka.topic": "testusers",
    "quickstart": "users",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "max.interval": 1000,
    "iterations": 10000000,
    "tasks.max": "1"
  }
}
> curl -X POST --header "Content-Type: application/json" "https://public%2Fdefault:${APIKEY}@${KAFKA-SERVICE-URL}/admin/kafkaconnect/connectors/" --data @datagen.json
{"name":"test","config":{"connector.class":"io.confluent.kafka.connect.datagen.DatagenConnector","kafka.topic":"testusers","quickstart":"users","key.converter":"org.apache.kafka.connect.storage.StringConverter","value.converter":"org.apache.kafka.connect.json.JsonConverter","value.converter.schemas.enable":"false","max.interval":"1000","iterations":"10000000","tasks.max":"1","name":"test"},"tasks":[],"type":"source"}

If you want to list the submitted connect for a double check, run the following command:

> curl "https://public%2Fdefault:${APIKEY}@${KAFKA-SERVICE-URL}/admin/kafkaconnect/connectors/"

["test"]

To create a data generator source connect named test, follow these steps:

Login to the StreamNative Cloud Console.
In the left navigation pane, click Connectors, then click the Kafka Sources tab.
Click the Create Kafka Source button:
Fill in the required fields and optional fields as you wish, and then Click the Submit button.

If you want to verify whether the data generator source connect has been created successfully, go back to the Connectors page, and you should see the created connector in the Kafka Sources tab, like below:

Create kafka connect with SMT

StreamNative Cloud supports Single Message Transformations (SMTs) for Kafka Connect. You can use SMTs to transform messages before they are written to the target system. The following example shows how to create a Datagen source connector named test on StreamNative Cloud using different tools. Please refer to the Kafka Connect SMTs to check the supported SMTs in StreamNative cloud.

snctl
kcctl
Rest API
Console

To create a data generator source connect named test with SMT, run the following command.

> cat datagen.json
{
  "name": "test",
  "config": {
    "connector.class": "io.confluent.kafka.connect.datagen.DatagenConnector",
    "kafka.topic": "testusers",
    "quickstart": "users",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "max.interval": 1000,
    "iterations": 10000000,
    "tasks.max": "1",
    "transforms": "InsertSource",
    "transforms.InsertSource.type": "org.apache.kafka.connect.transforms.InsertField$Value",
    "transforms.InsertSource.static.field": "data_source",
    "transforms.InsertSource.static.value": "test-file-source"
  }
}
> snctl kafka admin connect apply -f datagen.json --use-service-account

You should see the following output:

Created connector test

If you want to verify whether the data generator source connect has been created successfully, run the following command:

snctl kafka admin connect get connectors

You should see the following output:

 NAME                  TYPE     STATE        TASKS
 test                  source   RUNNING      0: RUNNING

To create a data generator source connect named test with SMT, run the following command.

> cat datagen.json
{
  "name": "test",
  "config": {
    "connector.class": "io.confluent.kafka.connect.datagen.DatagenConnector",
    "kafka.topic": "testusers",
    "quickstart": "users",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "max.interval": 1000,
    "iterations": 10000000,
    "tasks.max": "1",
    "transforms": "InsertSource",
    "transforms.InsertSource.type": "org.apache.kafka.connect.transforms.InsertField$Value",
    "transforms.InsertSource.static.field": "data_source",
    "transforms.InsertSource.static.value": "test-file-source"
  }
}
> kcctl apply -f datagen.json

You should see the following output:

Created connector test

If you want to verify whether the data generator source connect has been created successfully, run the following command:

kcctl get connectors

You should see the following output:

 NAME                  TYPE     STATE        TASKS
 test                  source   RUNNING      0: RUNNING

To create a data generator source connect named test with SMT, run the following command.

> cat datagen.json
{
  "name": "test",
  "config": {
    "connector.class": "io.confluent.kafka.connect.datagen.DatagenConnector",
    "kafka.topic": "testusers",
    "quickstart": "users",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "max.interval": 1000,
    "iterations": 10000000,
    "tasks.max": "1",
    "transforms": "InsertSource",
    "transforms.InsertSource.type": "org.apache.kafka.connect.transforms.InsertField$Value",
    "transforms.InsertSource.static.field": "data_source",
    "transforms.InsertSource.static.value": "test-file-source"
  }
}
> curl -X POST --header "Content-Type: application/json" "https://public%2Fdefault:${APIKEY}@${KAFKA-SERVICE-URL}/admin/kafkaconnect/connectors/" --data @datagen.json
{"name":"test","config":{"connector.class":"io.confluent.kafka.connect.datagen.DatagenConnector","kafka.topic":"testusers","quickstart":"users","key.converter":"org.apache.kafka.connect.storage.StringConverter","value.converter":"org.apache.kafka.connect.json.JsonConverter","value.converter.schemas.enable":"false","max.interval":"1000","iterations":"10000000","tasks.max":"1","transforms":"InsertSource","transforms.InsertSource.type":"org.apache.kafka.connect.transforms.InsertField$Value","transforms.InsertSource.static.value":"test-file-source","name":"test"},"tasks":[],"type":"source"}

If you want to list the submitted connect for a double check, run the following command:

> curl "https://public%2Fdefault:${APIKEY}@${KAFKA-SERVICE-URL}/admin/kafkaconnect/connectors/"

["test"]

To create a data generator source connect named test with SMT, follow these steps:

Login to the StreamNative Cloud Console.
In the left navigation pane, click Connectors, then click the Kafka Sources tab.
Click the Create Kafka Source button:
Fill in the required fields and optional fields as you wish
Click the Advance Settings tab, and then fill in the SMT fields:
Click the Submit button.

Create kafka connect with secret

Some connects require sensitive information, such as passwords, token, to be passed to the connector. And you may not want to expose these sensitive information in the connector configuration. To solve this problem, you can use the following methods to pass sensitive information to the connector:

Create a secret For example, the Milvus sink connector requires a token to be passed to the connector. You can create a secret in the console UI and pass the secret name to the connector configuration.

The location should be the same as the region of your Pulsar cluster.The awsAccessKey and awsSecretKey is the field name, and the lambda-sink-secret can be any unique name you want to give to the secret.For a Milvus sink, we should create a secret with a token field.

Pass secrets to the connector configuration The following example shows how to create a Milvus sink connector named test on StreamNative Cloud using different tools.

snctl
kcctl
Rest API
Console

To create a Milvus sink connector named test, run the following command.

> cat milvus.json
{
  "name": "test",
  "config": {
    "connector.class": "com.milvus.io.kafka.MilvusSinkConnector",
    "public.endpoint": "http://dockerhost.default.svc.cluster.local:19530",
    "collection.name": "demo",
    "token": "${snsecret:miluvs-sec:token}",
    "topics": "kafka-milvus-input",
    "key.converter": "org.apache.kafka.connect.json.JsonConverter",
    "key.converter.schemas.enable": "false",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "tasks.max": "1"
  }
}
> snctl kafka admin connect apply -f milvus.json --use-service-account

The miluvs-sec is the name of the secret you created

You should see the following output:

Created connector test

If you want to verify whether the data generator source connect has been created successfully, run the following command:

snctl kafka admin connect get connectors

You should see the following output:

NAME                  TYPE     STATE        TASKS
test                  sink     RUNNING      0: RUNNING

To create a Milvus sink connector named test, run the following command.

> cat milvus.json
{
  "name": "test",
  "config": {
    "connector.class": "com.milvus.io.kafka.MilvusSinkConnector",
    "public.endpoint": "http://dockerhost.default.svc.cluster.local:19530",
    "collection.name": "demo"
    "token": "${snsecret:miluvs-sec:token}",
    "topics": "kafka-milvus-input",
    "key.converter": "org.apache.kafka.connect.json.JsonConverter",
    "key.converter.schemas.enable": "false",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "tasks.max": "1"
  }
}
> kcctl apply -f milvus.json

The miluvs-sec is the name of the secret you created

You should see the following output:

Created connector test

If you want to verify whether the data generator source connect has been created successfully, run the following command:

kcctl get connectors

You should see the following output:

NAME                  TYPE     STATE        TASKS
test                  sink     RUNNING      0: RUNNING

To create a Milvus sink connector named test, run the following command.

> cat milvus.json
{
  "name": "test",
  "config": {
    "connector.class": "com.milvus.io.kafka.MilvusSinkConnector",
    "public.endpoint": "http://dockerhost.default.svc.cluster.local:19530",
    "collection.name": "demo"
    "token": "${snsecret:miluvs-sec:token}",
    "topics": "kafka-milvus-input",
    "key.converter": "org.apache.kafka.connect.json.JsonConverter",
    "key.converter.schemas.enable": "false",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "value.converter.schemas.enable": "false",
    "tasks.max": "1"
  }
}
> curl -X POST --header "Content-Type: application/json" "https://public%2Fdefault:${APIKEY}@${KAFKA-SERVICE-URL}/admin/kafkaconnect/connectors/" --data @miluvs.json
{"name":"test","config":{"connector.class":"com.milvus.io.kafka.MilvusSinkConnector","topics":"kafka-milvus-input","public.endpoint":"http://dockerhost.default.svc.cluster.local:19530","collection.name":"demo","token":"${snsecret:miluvs-sec:token}","key.converter":"org.apache.kafka.connect.json.JsonConverte","key.converter.schemas.enable":"false","value.converter":"org.apache.kafka.connect.json.JsonConverter","value.converter.schemas.enable":"false","tasks.max":"1","name":"test"},"tasks":[],"type":"sink"}

If you want to list the submitted connect for a double check, run the following command:

> curl "https://public%2Fdefault:${APIKEY}@${KAFKA-SERVICE-URL}/admin/kafkaconnect/connectors/"

["test"]

To create a Milvus sink connect named test, follow these steps:

Login to the StreamNative Cloud Console.
In the left navigation pane, click Connectors, then click the Kafka Sinks tab.
Click the Create Kafka Sink button, and then choose the Milvus connect:
In the Authentication Secrets selection box, you can choose an existing secret or create a new secret.
Fill in the required fields and optional fields as you wish, and then Click the Submit button.

Create a custom kafka connect

Before creating a kafka connect, it’s highly recommended to do the following:

Check kafka connect availability to ensure the version number of the kafka connect you want to create is supported on StreamNative Cloud.
Go to StreamNative Hub and find the connect-specific docs of your version for configuration reference.

To create a custom kafka connect, you need to upload the connector jar/zip file to the StreamNative Cloud Package service first. Below are the steps:

Upload your connector file to Pulsar

snctl
Pulsarctl
Pulsar-admin

Upload packages

snctl pulsar admin packages upload function://public/default/mongo-connect-zip@v1 \
--path /tmp/mongodb-kafka-connect-mongodb-1.12.0.zip \
--description "mongodb kafka connect in zip format" \
--properties fileName=mongo-kafka-connect.zip \
--properties libDir=mongodb-kafka-connect-mongodb-1.12.0/lib

You should see the following output:

The package 'function://public/default/mongo-connect-zip@v1' uploaded from path '/tmp/mongodb-kafka-connect-mongodb-1.12.0.zip' successfully

the property libDir specifies the directory where the third-party libraries are located in the zip file.

You need to set the context for Pulsarctl first:

# create a context
pulsarctl context set ${context-name}  \
--admin-service-url ${admin-service-url} \
--issuer-endpoint ${issuerUrl} \
--audience urn:sn:pulsar:${orgName}:${instanceName} \
--key-file ${privateKey}

# activate oauth2
pulsarctl oauth2 activate

Replace the placeholder variables with the actual values that you can get when setting up client tools.

context-name: any name you want
admin-service-url: the HTTP service URL of your Pulsar cluster.
privateKey: the path to the downloaded OAuth2 key file.
issuerUrl: the URL of the OAuth2 issuer.
audience: the Uniform Resource Name (URN), which is a combination of the urn:sn:pulsar, your organization name, and your Pulsar instance name.
- ${orgName}: the name of your organization.
- ${instanceName}: the name of your instance.

Upload packages

pulsarctl packages upload function://public/default/mongo-connect-zip@v1 \
--path /tmp/mongodb-kafka-connect-mongodb-1.12.0.zip \
--description "mongodb kafka connect in zip format" \
--properties fileName=mongo-kafka-connect.zip \
--properties libDir=mongodb-kafka-connect-mongodb-1.12.0/lib

You should see the following output:

The package 'function://public/default/mongo-connect-zip@v1' uploaded from path '/tmp/mongodb-kafka-connect-mongodb-1.12.0.zip' successfully

the property libDir specifies the directory where the third-party libraries are located in the zip file.

./bin/pulsar-admin \
    --admin-url "${WEB_SERVICE_URL}" \
    --auth-plugin org.apache.pulsar.client.impl.auth.oauth2.AuthenticationOAuth2 \
    --auth-params '{"privateKey":"file://${privateKey}","issuerUrl":"${issuerUrl}","audience":"urn:sn:pulsar:${orgName}:${instanceName}}' \
    packages upload function://public/default/mongo-connect-zip@v1 \
    --path /tmp/mongodb-kafka-connect-mongodb-1.12.0.zip \
    --description "mongodb kafka connect in zip format" \
    --properties fileName=mongo-kafka-connect.zip \
    --properties libDir=mongodb-kafka-connect-mongodb-1.12.0/lib

Replace the placeholder variables with the actual values that you can get when setting up client tools.

admin-url: the HTTP service URL of your Pulsar cluster.
privateKey: the path to the downloaded OAuth2 key file.
issuerUrl: the URL of the OAuth2 issuer.
audience: the Uniform Resource Name (URN), which is a combination of the urn:sn:pulsar, your organization name, and your Pulsar instance name.
- ${orgName}: the name of your organization.
- ${instanceName}: the name of your instance.

You should see the following output:

The package 'function://public/default/mongo-connect-zip@v1' uploaded from path '/tmp/mongodb-kafka-connect-mongodb-1.12.0.zip' successfully

the property libDir specifies the directory where the third-party libraries are located in the zip file.

The following example shows how to create a custom mongodb source connect named mongo-source on StreamNative Cloud using different tools.

snctl
kcctl
Rest API

To create a custom mongodb source connect named mongo-source, run the following command.

> cat mongo.json
{
  "name": "mongo-source",
  "config": {
    "connector.class": "com.mongodb.kafka.connect.MongoSourceConnector",
    "connection.uri": "mongodb://mongo.default.svc.cluster.local:27017/?authSource=admin",
    "database": "kafka-mongo",
    "collection": "source",
    "key.converter.schemas.enable": false,
    "value.converter.schemas.enable": false,
    "sn.pulsar.package.url": "function://public/default/mongo-connect-zip@v1",
    "tasks.max": "1"
  }
}
> snctl kafka admin connect apply -f mongo.json --use-service-account

The sn.pulsar.package.url is the package url you uploaded to the StreamNative Cloud Package service.

You should see the following output:

Created connector mongo-source

If you want to verify whether the data generator source connect has been created successfully, run the following command:

snctl kafka admin connect get connectors

You should see the following output:

 NAME                  TYPE     STATE        TASKS
 mongo-source          source   RUNNING      0: RUNNING

To create a custom mongodb source connect named mongo-source, run the following command.

> cat mongo.json
{
  "name": "mongo-source",
  "config": {
    "connector.class": "com.mongodb.kafka.connect.MongoSourceConnector",
    "connection.uri": "mongodb://mongo.default.svc.cluster.local:27017/?authSource=admin",
    "database": "kafka-mongo",
    "collection": "source",
    "key.converter.schemas.enable": false,
    "value.converter.schemas.enable": false,
    "sn.pulsar.package.url": "function://public/default/mongo-connect-zip@v1",
    "tasks.max": "1"
  }
}
> kcctl apply -f mongo.json

The sn.pulsar.package.url is the package url you uploaded to the StreamNative Cloud Package service.

You should see the following output:

Created connector mongo-source

If you want to verify whether the data generator source connect has been created successfully, run the following command:

kcctl get connectors

You should see the following output:

 NAME                  TYPE     STATE        TASKS
 mongo-source          source   RUNNING      0: RUNNING

To create a custom mongodb source connect named mongo-source, run the following command.

> cat mongo.json
{
  "name": "mongo-source",
  "config": {
    "connector.class": "com.mongodb.kafka.connect.MongoSourceConnector",
    "connection.uri": "mongodb://mongo.default.svc.cluster.local:27017/?authSource=admin",
    "database": "kafka-mongo",
    "collection": "source",
    "key.converter.schemas.enable": false,
    "value.converter.schemas.enable": false,
    "sn.pulsar.package.url": "function://public/default/mongo-connect-zip@v1",
    "tasks.max": "1"
  }
}
> curl -X POST --header "Content-Type: application/json" "https://public%2Fdefault:${APIKEY}@${KAFKA-SERVICE-URL}/admin/kafkaconnect/connectors/" --data @mongo.json
{"name":"mongo-source","config":{"connector.class":"com.mongodb.kafka.connect.MongoSourceConnector","connection.uri":"mongodb://mongo.default.svc.cluster.local:27017/?authSource=admin","database":"kafka-mongo","collection":"source","key.converter.schemas.enable":false,"value.converter.schemas.enable":false,"sn.pulsar.package.url":"function://public/default/mongo-connect-zip","tasks.max":"1"}}

If you want to list the submitted connect for a double check, run the following command:

> curl "https://public%2Fdefault:${APIKEY}@${KAFKA-SERVICE-URL}/admin/kafkaconnect/connectors/"

["mongo-source"]

Set resources for kafka connect

You can use below two configs to set the resources for the kafka connect to control the CPU and memory usage of the connector:

sn.cpu: The number of CPU cores to allocate to the connector, default to 0.5.
sn.memory: The bytes of memory to allocate to the connector, default to 2147483648 (2G).

You need to upgrade your Pulsar cluster to v3.0.8.4+, v3.3.3.4+ or v4.0.1.3+ to use the sn.cpu and sn.memory configs.

Tune Kafka Connect clients

You can tune the Kafka client behavior for an individual connector by adding connector-level override settings. These settings are useful when one connector needs different throughput, latency, or retry behavior than the default Kafka Connect worker settings. Use the following prefixes in the connector configuration:

producer.override.*: Overrides Kafka producer settings. Use this prefix mainly for source connectors that write records to Kafka topics. Common settings include compression.type, batch.size, linger.ms, and acks.
consumer.override.*: Overrides Kafka consumer settings. Use this prefix for sink connectors that read records from Kafka topics. Common settings include max.poll.records, fetch.min.bytes, fetch.max.wait.ms, and auto.offset.reset.
admin.override.*: Overrides Kafka administrative client settings. Use this prefix when the connector needs different administrative client behavior, such as for topic creation or dead-letter queue operations. Common settings include request.timeout.ms, retry.backoff.ms, and default.api.timeout.ms.

For example, the following sink connector configuration increases the consumer batch size and adjusts admin client timeouts:

{
  "name": "jdbc-sink",
  "config": {
    "connector.class": "io.confluent.connect.jdbc.JdbcSinkConnector",
    "topics": "orders",
    "tasks.max": "1",
    "consumer.override.max.poll.records": "1000",
    "consumer.override.fetch.min.bytes": "1048576",
    "consumer.override.fetch.max.wait.ms": "500",
    "admin.override.request.timeout.ms": "30000",
    "admin.override.retry.backoff.ms": "500"
  }
}

The following source connector example tunes the producer used to write records to Kafka:

{
  "name": "mongo-source",
  "config": {
    "connector.class": "com.mongodb.kafka.connect.MongoSourceConnector",
    "tasks.max": "1",
    "producer.override.compression.type": "gzip",
    "producer.override.batch.size": "131072",
    "producer.override.linger.ms": "20",
    "producer.override.acks": "all"
  }
}

Schema Registry Support

Kafka Connect supports using schema registry to save Avro/Protobuf/Json schema for the value and key. And StreamNative has an internal schema registry which can be used without complex configurations. To use it, you just need to set the following configuration in the connector configuration:

value.converter.schema.registry.internal: true: if you want to use the internal schema registry for the value converter.
key.converter.schema.registry.internal: true: if you want to use the internal schema registry for the key converter.

Get Started

Clusters

Data Streams

Process

Connect

Lakehouse

Governance

Pulsar Clients

MQTT Clients

Tools

Pulsar Changelogs

Prerequisites

Create a built-in kafka connect

Create kafka connect with SMT

Create kafka connect with secret

Create a custom kafka connect

Upload your connector file to Pulsar

Set resources for kafka connect

Tune Kafka Connect clients

Schema Registry Support

​Prerequisites

​Create a built-in kafka connect

​Create kafka connect with SMT

​Create kafka connect with secret

​Create a custom kafka connect

​Upload your connector file to Pulsar

​Set resources for kafka connect

​Tune Kafka Connect clients

​Schema Registry Support

Prerequisites

Create a built-in kafka connect

Create kafka connect with SMT

Create kafka connect with secret

Create a custom kafka connect

Upload your connector file to Pulsar

Set resources for kafka connect

Tune Kafka Connect clients

Schema Registry Support