Create and execute queries by using a compute solution that leverages SQL serverless and Spark cluster

If you’re looking to perform data engineering tasks on Microsoft Azure, you can leverage a compute solution that combines SQL Serverless and Spark clusters. SQL Serverless allows you to query data stored in different formats, while Spark clusters provide distributed computing capabilities. In this article, we’ll guide you through the process of utilizing these technologies to create and execute queries on Azure.

Step 1: Create an Azure SQL Database

To begin, follow these steps to create an Azure SQL Database:

• Open the Azure portal and navigate to the SQL Databases service.
• Click on “Create” and provide the necessary details such as server, database name, and resource group.
• Choose the appropriate pricing tier based on your requirements.
• Once created, make note of the server name and database name for future reference.

Step 2: Load Data into Azure Storage

If your data is not already in Azure Storage, follow these steps to upload it:

• Use the Azure portal or Azure Storage Explorer to upload your data into Azure Storage.
• Take note of the location of the data files, including the container path and file names.

Step 3: Create an Azure Synapse Workspace

Next, create an Azure Synapse workspace by following these steps:

• Open the Azure portal and navigate to the Synapse workspace service.
• Click on “Create” and provide the required details such as subscription, resource group, workspace name, and region.
• Choose the appropriate pricing tier and other settings.
• Once created, navigate to the Synapse workspace.

Step 4: Provision a Spark Pool

Now it’s time to provision a Spark pool. Here’s how:

• In the Synapse workspace, go to the “Manage” hub and select “Apache Spark pools”.
• Click on “New” to create a new Spark pool.
• Provide a name for the pool and select an appropriate node size and number of nodes based on your workload.
• Configure advanced options if needed.
• Wait for the pool to be provisioned.

Step 5: Create an External Data Source

To create an external data source, perform the following steps:

• In the Synapse workspace, navigate to the “Data” hub and select “Linked” > “Linked services”.
• Click on “New” and choose the “Azure Blob Storage” connector.
• Provide a name for the linked service and enter the required details, including the storage account name and the account key or shared access signature (SAS) token.
• Test the connection and save the linked service.

Step 6: Create a Serverless SQL Pool

Time to create a serverless SQL pool. Follow these steps:

• In the Synapse workspace, go to the “Data” hub and select “New” > “SQL script”.
• Provide a name for the pool and choose “SQL On-demand” as the pool type.
• Write your SQL queries to analyze the data.

Here’s an example of executing a simple SQL query using the serverless SQL pool:

%%sql
SELECT column1, column2
FROM external_table
WHERE column3 = ‘value’

Step 7: Submit Spark Jobs

Now, let’s submit Spark jobs to process your data. Follow these steps:

• In the Synapse workspace, go to the “Develop” hub and select “Integration” > “Notebooks”.
• Create a new notebook or open an existing one.
• Write Spark code to process your data using the provisioned Spark pool.

Here’s an example of using Spark to read data from Azure Storage and perform transformations:

# Read data
data = spark.read.format(“csv”).option(“header”, “true”).load(“abfss://[email protected]/path/to/file.csv”)

# Perform transformations
transformedData = data.select(“column1”, “column2”).filter(data.column3 == “value”)

# Write data
transformedData.write.mode(“overwrite”).parquet(“abfss://[email protected]/path/to/output”)

Step 8: Monitor and Optimize Your Compute Solution

Lastly, it’s important to monitor and optimize your compute solution. Here are some recommendations:

• In the Synapse workspace, navigate to the “Monitor” hub to view job status, logs, and performance metrics.
• Analyze query and job performance to identify any bottlenecks or opportunities for optimization.
• Consider scaling the Spark pool or refining the data storage to achieve better performance if needed.

By following these steps, you can leverage SQL Serverless and Spark clusters within an Azure Synapse workspace to create and execute queries for your data engineering tasks. This powerful compute solution provides the flexibility to work with various data formats, scale as needed, and perform complex data transformations efficiently in a distributed environment.