Healthcare Data Lakes on Azure Synapse Analytics

Question

Pulumi · Accepted Answer

To implement Healthcare Data Lakes using Azure Synapse Analytics, we'll need to create a combination of Azure resources that facilitate the storage, management, and analysis of large sets of healthcare data. Azure Synapse Analytics provides big data and data warehousing solutions that allow you to ingest, explore, prepare, manage, and serve data for immediate BI and machine learning needs.

Below you'll find a Pulumi program written in Python that sets up a basic infrastructure for a Healthcare Data Lake using Azure Synapse Analytics, which includes:

1. **Synapse Workspace** - Central space where you manage and monitor all the Azure Synapse Analytics resources.
2. **Synapse SQL Pool (formerly SQL Data Warehouse)** - A managed, scalable data warehouse that you use to run complex queries on large sets of data and you pay for the compute that you use.
3. **Storage Account** - Where the data is stored. Synapse can read and write data to Azure Data Lake Storage.
4. **Synapse Spark Pool (Big Data Analysis)** - Azure Synapse Analytics supports Apache Spark, which is an analytics engine for big data processing.

Let's explore the code that sets up this environment:

```python
import pulumi
from pulumi_azure_native import resources, synapse, storage

# Create an Azure Resource Group to contain all the resources
resource_group = resources.ResourceGroup('healthcare-data-lakes-rg')

# Create an Azure Synapse Workspace
synapse_workspace = synapse.Workspace('synapse-workspace',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    identity=synapse.ManagedIdentityArgs(
        type="SystemAssigned"
    )
)

# Create a Synapse SQL Pool 
sql_pool = synapse.SqlPool('sql-pool',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku=synapse.SkuArgs(
        name="DW100c"
    ),
    workspace_name=synapse_workspace.name,
    create_mode="Default"
)

# Create a Synapse Spark Pool
spark_pool = synapse.BigDataPool('spark-pool',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    workspace_name=synapse_workspace.name,
    node_size="Small",
    node_count=3
)

# Create an Azure Storage Account for the Data Lake
storage_account = storage.StorageAccount('datalakestorage',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    kind="StorageV2",
    sku=storage.SkuArgs(
        name="Standard_LRS"
    ),
    is_hns_enabled=True  # Enabling Hierarchical Namespace for Data Lake Gen2 functionality
)

# Exporting output properties of the resources created
pulumi.export('synapse_workspace_name', synapse_workspace.name)
pulumi.export('sql_pool_name', sql_pool.name)
pulumi.export('spark_pool_name', spark_pool.name)
pulumi.export('storage_account_name', storage_account.name)
```

This program starts by importing the necessary Pulumi modules for Azure resources. It then defines and creates the following resources:
- An **Azure Resource Group** as a container for all related resources for the data lake.
- A **Synapse Workspace** that will serve as the central hub for Synapse Analytics services.
- A **Synapse SQL Pool** which provides SQL capabilities for data warehousing.
- A **Synapse Spark Pool** for big data analytics using Apache Spark.
- A **Storage Account** with Hierarchical Namespace enabled, suited for Data Lake Gen2 functionality, needed to store large amounts of data in a hierarchical file system.

Each resource is associated with the created resource group, and relevant properties are configured accordingly. For example, the `ManagedIdentityArgs` and `sku` properties are set up to define the identity type of the Synapse Workspace and the performance level of the SQL pool, respectively.

Finally, the program exports the names of the created resources which can be used to reference them in other contexts or stack outputs.

With this basic setup, you can start ingesting healthcare data into the Storage Account and then use Synapse Analytics to perform various data operations ranging from batch processing, data exploration, machine learning, and data warehousing.