1. Yandex Data Proc for Big Data AI Processing.


    To create a big data processing cluster on Yandex.Cloud using Pulumi for AI processing, you'll use the yandex.DataprocCluster resource. Yandex Dataproc is a managed service that allows you to easily create and manage clusters equipped with data processing tools like Hadoop, Spark, and others which are essential for big data and AI workloads.

    Below, you will find a Pulumi program in Python that sets up a Dataproc cluster on Yandex.Cloud. The program defines a cluster with the necessary resources, including a specified number of hosts and their configurations. This code assumes you have necessary permissions and that your Pulumi environment is already configured for Yandex.Cloud (pulumi_yandex).

    Here's what each part of the program does:

    1. Imports and Initialization: Import pulumi_yandex package and initialize a new Pulumi project.

    2. Cluster Configuration: Defines the cluster's configuration, including the Hadoop settings, number of subclusters (groups of hosts with the same role, such as master or data), and resources like CPU and RAM.

    3. DataprocCluster Resource: Creates a DataprocCluster resource with the specified configuration.

    4. Export Output: At the end of the program, export the DataprocCluster ID so that it can be referenced outside of Pulumi if needed.

    Let's look at the Pulumi program:

    import pulumi import pulumi_yandex as yandex # Define the configuration for Hadoop services hadoop_config = { # Services like HDFS, YARN, MapReduce, etc., which will be installed # on the cluster "services": ["HDFS", "YARN", "MAPREDUCE", "HIVE", "SPARK"], } # Define the specification for a subcluster that will run the master node master_subcluster_spec = { "name": "master-subcluster", "role": "master", "subnet_id": "YOUR_SUBNET_ID", # Replace with your subnet ID. "resources": { "resource_preset_id": "s2.small", "disk_size": 15, "disk_type_id": "network-hdd", }, "hosts_count": 1, } # Define the specification for a subcluster that will run data nodes data_subcluster_spec = { "name": "data-subcluster", "role": "data", "subnet_id": "YOUR_SUBNET_ID", # Replace with your subnet ID. "resources": { "resource_preset_id": "s2.medium", "disk_size": 100, "disk_type_id": "network-hdd", }, "hosts_count": 2, } # Create a Yandex Data Proc cluster dataproc_cluster = yandex.DataprocCluster("ai-dataproc-cluster", folder_id="YOUR_FOLDER_ID", # Replace with your Yandex.Cloud folder ID. zone_id="ru-central1-a", # This is an example zone ID, choose the appropriate zone. cluster_config={ "hadoop": hadoop_config, "version_id": "2.0", # The version of Data Proc cluster "subcluster_specs": [master_subcluster_spec, data_subcluster_spec], }, service_account_id="YOUR_SERVICE_ACCOUNT_ID", # Replace with your Yandex.Cloud service account ID. deletion_protection=False, ) # Export the ID of the cluster pulumi.export("dataproc_cluster_id", dataproc_cluster.id)

    In this program, replace YOUR_SUBNET_ID, YOUR_FOLDER_ID, and YOUR_SERVICE_ACCOUNT_ID with your actual Yandex.Cloud subnet ID, folder ID, and service account ID. You will also want to adjust the resource presets and disk types/sizes according to your processing needs.

    The choice of services included in hadoop_config should match your big data and AI application requirements. In this case, we have included common services such as HDFS, YARN, MapReduce, Hive, and Spark which are typically used for data processing tasks.

    When you run this Pulumi program, it will provision a new Data Proc cluster tailored for big data AI processing within your specified Yandex.Cloud environment.