Data Ingestion for AI Pipelines with Kafka on Kubernetes

Question

Pulumi · Accepted Answer

If you aim to set up a data ingestion system for AI pipelines using Kafka on Kubernetes, you'll need a Kafka cluster to capture and transport data, and a Kubernetes cluster to manage and scale your data processing workloads.

For Kafka, we'll use Aiven for Kafka, which provides Kafka as a service, ensuring that we have a managed, scalable, and fault-tolerant Kafka cluster without needing to manually configure and manage the Kafka components.

Kubernetes will serve as the container orchestration layer that will host the AI services and any other microservices that might interact with Kafka. We'll use Pulumi to orchestrate both Aiven for Kafka and Kubernetes clusters.

In this Pulumi program, we will create:

Aiven Kafka service using aiven.Kafka.
Kafka topics to store and organize the data using aiven.KafkaTopic.
A Kubernetes cluster, which we'll simplify by using a managed service like Google Kubernetes Engine (GKE) represented here by gcp.container.Cluster.

Here's a complete Pulumi program that sets up a data ingestion pipeline for AI with Kafka on Kubernetes:

import pulumi
import pulumi_aiven as aiven
import pulumi_gcp as gcp

# Create an Aiven Kafka service
kafka_service = aiven.Kafka("kafkaService",
    plan="business-4",                # Select a suitable plan based on your needs.
    project="<aiven-project-name>",   # Specify your Aiven project name.
    cloud_name="google-europe-west1", # Specify the cloud and region where the Kafka service should be hosted.
    maintenance_window_dow="sunday",  # Set maintenance window (day of week).
    maintenance_window_time="10:00:00", # Set maintenance window (time of day).

    # Define Kafka User Config
    kafka_user_config=aiven.KafkaUserConfigArgs(
        kafka_rest=True,             # Enable Kafka REST API.
        kafka_version="2.6",         # Specify Kafka version.
        kafka=aiven.KafkaUserConfigArgsKafkaArgs(
            auto_create_topics_enable=True # Enable topics to be auto-created.
        )
    )
)

# Create Kafka topics
kafka_topic = aiven.KafkaTopic("kafkaTopic",
    project="<aiven-project-name>",  # Specify the same Aiven project name.
    service_name=kafka_service.name, # Reference the created Kafka service.
    topic_name="data-ingest",        # Topic name for data ingestion.
    partitions=3,                    # Set the number of partitions.
    replication=2                    # Set the replication factor.
)

# Create a Google Kubernetes Engine (GKE) cluster to run our AI services
k8s_cluster = gcp.container.Cluster("gkeCluster",
    initial_node_count=3,            # Start with 3 nodes
    node_config=gcp.container.ClusterNodeConfigArgs(
        machine_type="n1-standard-1" # Use a standard machine type, select as per your AI workloads' demand.
    )
)

# Export the Kafka Service URI
pulumi.export("kafka_service_uri", kafka_service.service_uri)

# Export the GKE cluster name
pulumi.export("k8s_cluster_name", k8s_cluster.name)

The program starts by importing the required Pulumi packages.

It then creates a Kafka service on Aiven, specifying the plan, project name, cloud, and region. The maintenance window is also set, which is a time when Aiven can perform maintenance tasks without interrupting your workloads. The Kafka user config enables the REST API and auto-creation of topics. Adjust the cloud_name and project values to match your Aiven account details.

Next, we create a Kafka topic. This topic will be used for data ingestion, and you can set the number of partitions and the replication factor based on your throughput needs and fault tolerance requirements.

After setting up the Kafka components, a GKE cluster is created to run our AI services. The initial_node_count and machine_type are configurable parameters to match your workload demands.

Finally, we export the important information: the Kafka service URI and the GKE cluster name. These outputs can be used to connect your services and deploy workloads on Kubernetes.

Before running this program:

Ensure you have an account with Aiven and have set up the necessary access credentials in Pulumi using pulumi config set.
Configure the Google Cloud provider with access to your GCP account where you want to create the GKE cluster.
Replace placeholder values such as <aiven-project-name> with actual values from your service provider.