Redpanda Connect Quickstart

Learn how to build, run, and update a data pipeline on a Redpanda Cloud cluster using Redpanda Connect.

This quickstart introduces you to Redpanda Connect for Redpanda Cloud. Using a single YAML configuration file, you can quickly build streaming data pipelines from scratch. No third-party connectors are required.

Redpanda Connect is currently in a limited availability (LA) release for BYOC clusters. While it is also available as a beta feature for Serverless clusters, it is not suitable for production deployments.

Prerequisites

A Redpanda Cloud account for Serverless or standard BYOC (not customer-managed VPC). If you don’t already have an account, sign up for a free trial.

Before you start

Create the cluster, topic, and user you need to build and run your data pipeline.

Serverless
BYOC

Log in to Redpanda Cloud.
On the Clusters page, click Create cluster, then click Create Serverless cluster.
For cluster settings, enter connect-quickstart for the cluster name.
Use the default values for the remaining properties, then click Create.

Log in to Redpanda Cloud.
On the Clusters page, click Create cluster, then click Create BYOC cluster.
On the Cluster settings page, enter connect-quickstart for the cluster name.
Select your cloud provider, then click Next.
On the Networking page, select a Private connection type and choose a CIDR range, which does not overlap with your existing VPCs or your Redpanda network.
Click Next.

Wait while your cluster is created.

To complete your setup:

Go to the Topics page, click Create topic and enter processed-emails for the topic name. Use default values for the remaining properties and click Create and then Close.
Go to the Security page, and click Create user. Enter the username connect. Use the default values for the remaining properties. Remember to take a note of your password.
Click Create and Done.
Stay on the Access control page and click the ACLs tab.
Select the connect user you have just created. Click Allow all operations and then scroll down to click OK.
Finally, go to the Overview page and click the Kafka API tab.
Copy the bootstrap server URL into a text file. You will need it for the next steps.

Build your data pipeline

Configure your first data pipeline on the connect-quickstart cluster.

All Redpanda Connect configurations use a YAML file split into three sections:

Section In this data pipeline

Section	In this data pipeline
The `input` or data source	A fake data source that generates a batch of email messages every second, populated with an ID, paragraph, email address, and a user name.
The `pipeline` with one or more `processors`	A `mutation` processor to add a title to every email message it processes.
The `output` or data sink	A `kafka_franz` output that writes messages to the connect-output topic on your cluster.

The input or data source

A fake data source that generates a batch of email messages every second, populated with an ID, paragraph, email address, and a user name.

The pipeline with one or more processors

A mutation processor to add a title to every email message it processes.

The output or data sink

A kafka_franz output that writes messages to the connect-output topic on your cluster.

Go to the Connectors page on your cluster and click the Redpanda Connect tab.
Click Create pipeline.
In Pipeline name, enter emailprocessor-pipeline and add a short description. For example, Transforms email data using a mutation processor.

In the Configuration box, paste the following configuration.

input:
 generate:
   interval: 1s
   mapping: |
     root.id = uuid_v4()
     root.user.name = fake("name")
     root.user.email = fake("email")
     root.content = fake("paragraph")

pipeline:
 processors:
   - mutation: |
       root.title = "PRIVATE AND CONFIDENTIAL"

output:
 kafka_franz:
   seed_brokers:
     - <bootstrap-server-url>
   sasl:
     - mechanism: SCRAM-SHA-256
       password: <cluster-password>
       username: connect
   topic: processed-emails
   tls:
     enabled: true

Replace the following placeholders:

<bootstrap-server-url>: The bootstrap server address you copied in Before you start.
<cluster-password>: The password of the connect user you set up in Before you start.

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Click Create. Your pipeline details are displayed and the pipeline state changes from Starting to Running, which may take a few minutes. If you don’t see this state change, refresh your page.

Redpanda Connect starts to ingest, process, and write transformed email messages to the processed-emails topic.
After a few seconds, select the pipeline and click Stop.

Explore the processed data and logs

Take a look at the data your pipeline has already processed, and the logs that are available for troubleshooting.

To see the pipeline output:

Go to the Topics page and select the processed-emails topic.

Select any message to see the email message fields generated by the pipeline input, along with a title field added by the mutation processor. All messages have the following structure:

{
    "content": "Aliquam quidem tempore expedita debitis ab. Officiis optio eveniet ab magni commodi...",
    "id": "35522c66-6fcd-47da-b97b-857b983477d1",
    "title": "PRIVATE AND CONFIDENTIAL",
    "user": {
        "email": "oCcXPTh@RrKHZRQ.info",
        "name": "King Francis Torphy"
    }
}

To view the logs:

Return to the Connectors page on your cluster and select the emailprocessor-pipeline.

Click the Logs tab and select each of the four log messages. You can see the sequence of events that start the data pipeline. For example, you can see when Redpanda Connect starts to write data to the topic:

{
    "instance_id": "cr3j2rab2tks83v3gbh0",
    "label": "",
    "level": "INFO",
    "message": "Output type kafka_franz is now active",
    "path": "root.output",
    "pipeline_id": "cr3j2r6hqokqcph9p4b0",
    "time": "2024-08-22T12:39:09.729899336Z"
}

Update your pipeline

Now try adding custom logging and an extra data transformation step to your configuration. You can make the updates while your data pipeline is running.

Select the Configuration tab of your data pipeline.
Click Start and wait for your pipeline to start running.
Click Edit and overwrite the processors section of your configuration with the following snippet.
```
  processors:
    - mutation: |
        root.title = "PRIVATE AND CONFIDENTIAL"
        root.user.name = root.user.name.uppercase()
    - log:
       level: INFO
       message: 'Processed email for ${!this.user.name}'
       fields_mapping: |
         root.reason = "SUCCESS"
         root.id = this.id
```
The snippet includes new configuration to:
- Transform the name of each email sender to uppercase.
- Change the logging level
- Write a summary message and the reason for every email message processed, using the log processor.
Click Update.
After a few seconds, click Stop.

Click the Logs tab and select the most recent (final) log message. You can see the custom logging fields along with the uppercase user’s name.

{
    "id": "f64d1f1a-2d76-47ad-a215-52410ab4e22f",
    "instance_id": "cr3ncrvom8ofl3bn3rk0",
    "label": "",
    "level": "INFO",
    "message": "Processed email for MISS IMELDA REICHERT",
    "path": "root.pipeline.processors.1",
    "pipeline_id": "cr3me2uhqokqcph9p4bg",
    "reason": "SUCCESS",
    "time": "2024-08-22T17:33:46.676903284Z"
}

Clean up

When you’ve finished experimenting with your data pipeline, you can delete the pipeline, topic, and cluster you created for this quickstart.

On the Connectors page, select your pipeline.
Click Delete and confirm your deletion to remove the data pipeline and associated logs.
On the Topics page, delete the processed-emails topic.
Go back to the Clusters page and delete the connect-quickstart cluster.

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