Scalable Storage for AI Training Data with GCP Bigtable

Question

Pulumi · Accepted Answer

To create a scalable storage solution for AI training data on GCP, we'll use Google Cloud Bigtable. Bigtable is designed for large amounts of data and high throughput, making it suitable for storing AI training data that generally requires quick access to huge datasets.

First, let's go over the resources we'll be using:

1. **Bigtable Instance (`gcp.bigtable.Instance`)**: This is the primary container for data in Bigtable. It can contain one or more clusters and defines the configuration for the storage. We'll create an instance suited for production with a cluster configured for high performance.
   
2. **Bigtable Table (`gcp.bigtable.Table`)**: Within the instance, tables store data. Each table is composed of rows and columns, and optionally, column families which group together various columns.

Here's a Pulumi program in Python to set up such a storage system:

```python
import pulumi
import pulumi_gcp as gcp

# Create a Bigtable Instance for our AI Data
bigtable_instance = gcp.bigtable.Instance("ai-bigtable-instance",
    project="your-gcp-project-id", # Replace with your GCP project ID
    deletion_protection=False,  # This is set to false for demonstration purposes. Set to true for production environments.
    clusters=[{
        "clusterId": "ai-bigtable-cluster",
        "zone": "us-central1-b",
        "numNodes": 3,  # Starting with 3 nodes for scalability.
        "storageType": "SSD",  # SSD storage provides low-latency read/writes.
        "autoscalingConfig": {
            "maxNodes": 10,  # Set max nodes for autoscaling according to expected workloads.
            "minNodes": 3,
            "cpuTarget": 75  # Target 75% CPU utilization before scaling out.
        }
    }],
    instance_type="PRODUCTION",  # This denotes a production instance (as opposed to DEVELOPMENT).
)

# Create a Bigtable Table within our Instance
bigtable_table = gcp.bigtable.Table("ai-bigtable-table",
    instance_name=bigtable_instance.instance_name,  # Link to our instance above.
    column_families=[{
        "family": "training-data",  # Define a column family for training data.
    }]
)

# Export the Bigtable instance and table names
pulumi.export("bigtable_instance_name", bigtable_instance.name)
pulumi.export("bigtable_table_name", bigtable_table.name)
```

Let's walk through what each part of the code does:

- We import the required Pulumi and GCP modules. The `pulumi_gcp` library contains all the classes needed to create GCP resources.
- We instantiate a `gcp.bigtable.Instance`, passing parameters that define the project ID, cluster settings (like the number of nodes and the type of storage), and enabling autoscaling to accommodate varying workloads.
- We create a `gcp.bigtable.Table` inside our instance. This table will hold the training data for AI applications. We define a column family within the table; you can add more depending on how your data is structured.
- We use `pulumi.export` to output the names of the Bigtable instance and table so that we can reference them later if needed.

Notice that we have not set deletion protection for the Bigtable instance, which means it can be easily deleted. In a production environment, you would want to set deletion protection to `True` to prevent accidental deletions of your instance.

Running this Pulumi program will provision a scalable, managed NoSQL database service on GCP, suitable for the sort of high-throughput, scalable workloads that AI training typically demands.