1. Creating a hybrid cloud analytics solution integrating on-premises databases with AWS Redshift using AWS Data Migration Service

    Python

    Based on your request, it seems like we will need several steps to accomplish this task as it involves a few different AWS services.

    Here is the high-level overview:

    1. Create an Amazon Redshift Cluster (using pulumi_aws.redshift.Cluster). Your analytics workloads will run in this Redshift database.

    2. Create an AWS DMS (Data Migration Service) instance (using pulumi_aws.dms.ReplicationInstance).

    3. Create a DMS endpoint for both your on-premises database and the Redshift database (using pulumi_aws.dms.Endpoint).

    4. Create a DMS replication task (using pulumi_aws.dms.ReplicationTask), that will defines the migration type and replication rules (replication job or chore).

    Now let's put that into a Pulumi program.

    The following is a rough skeleton for the Python Pulumi program. Please note that this is a high-level example and not a fully-working program. You need to provide appropriate configurations options replacing the TODOs.

    import pulumi
from pulumi_aws import redshift, dms

# Create a Redshift cluster
redshift_cluster = redshift.Cluster("redshiftCluster",
    cluster_identifier="redshift-cluster-1",
    database_name="mydb",
    master_username="todo_replace_with_username",
    master_password="todo_replace_with_password",
    node_type="dc2.large",
    cluster_type="single-node")

# Create DMS replication instance
dms_replication_instance = dms.ReplicationInstance("dmsReplicationInstance",
    replication_instance_id="test-dms-replication-instance-id",
    replication_instance_class="dms.r4.large")

# Create DMS source (on-premise database) endpoint
dms_source_endpoint = dms.Endpoint("dmsSourceEndpoint",
    endpoint_id="test-dms-source-endpoint",
    endpoint_type="source",
    # TODO - you'll need to configure the correct engine_name
    # and the connection info such as server_name, username, password, etc.
    engine_name="postgres",
    server_name="todo_replace_with_on_premise_db_host",
    username="todo_replace_with_on_premise_db_username",
    password="todo_replace_with_on_premise_db_password",
    database_name="todo_replace_with_on_premise_db_name")

# Create DMS target (Redshift) endpoint
dms_target_endpoint = dms.Endpoint("dmsTargetEndpoint",
    endpoint_id="test-dms-target-endpoint",
    endpoint_type="target",
    # TODO - you'll need to configure the correct engine_name
    # and the connection info such as server_name, username, password, etc.
    engine_name="redshift",
    server_name=redshift_cluster.endpoint,
    username="todo_replace_with_redshift_username",
    password="todo_replace_with_redshift_password",
    database_name="mydb")

# Create DMS migration task
dms_replication_task = dms.ReplicationTask("dmsReplicationTask",
    migration_type="full-load-and-cdc",
    table_mappings="todo-replace-with-table-mapping",
    replication_task_id="test-dms-replication-task-id",
    source_endpoint_arn=dms_source_endpoint.arn,
    target_endpoint_arn=dms_target_endpoint.arn,
    replication_instance_arn=dms_replication_instance.arn)

# Export the Redshift endpoint
pulumi.export('redshiftEndpoint', redshift_cluster.endpoint)

    The table_mappings attribute should follow AWS DMS's JSON format for table mappings (selecting schemas, tables, and columns, and specifying how to load and replicate data). You can find more information in the AWS DMS documentation.

    For more detailed information about each resource, please visit their corresponding links:

    Please replace all placeholders (such as todo_replace_with_...) with appropriate values as per your environment.


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