Building a data pipeline with Azure Databricks

Posted on by Panther

Data pipelines are a critical component in any data-centric organization. It’s essential to have a streamlined process in place that can efficiently and effectively process large volumes of data, transform it into a workable format, and then deliver it to downstream applications for analysis and consumption.

One of the best ways to build a data pipeline is by using Azure Databricksโ€”a fast, easy, and collaborative Apache Spark-based analytics platform. In this tutorial, we will walk you through the process of building a data pipeline with Azure Databricks.

Prerequisites

Before we begin, you will require the following:

  • An Azure subscription
  • An Azure Databricks workspace
  • Access to a storage account
  • A dataset to use for this exercise

Creating an Azure Databricks Cluster

The first step in building a data pipeline with Azure Databricks is to create an Azure Databricks cluster. A Databricks cluster is a managed cloud resource that enables data processing and machine learning workloads.

  1. Navigate to your Azure Databricks workspace.
  2. Click the “Clusters” tab.
  3. Click the “Create Cluster” button.
  4. Configure your cluster settings based on your requirements, such as the cluster name, cluster type, and the number of worker nodes.
  5. Click “Create Cluster.”

Creating an Azure Data Factory

The next step is to create an Azure Data Factory. Azure Data Factory is a cloud-based data integration service that allows you to create data pipelines that can move and transform data of all shapes and sizes.

  1. Navigate to your Azure portal.
  2. Click “Create a Resource.”
  3. Search for “Data Factory.”
  4. Click “Create.”
  5. Fill in the required details, such as the name, subscription, and resource group.
  6. Choose the version of the service you want to use.
  7. Click “Create.”

Preparing Your Dataset

Before moving forward, we need to have a dataset to work with. In this tutorial, we will be using a sample Excel file containing some basic patient information.

  1. Create a new Azure storage account.
  2. Navigate to the “Containers” tab.
  3. Click “Create Container.”
  4. Give your container a name, such as “patient-data.”
  5. Upload the sample Excel file to the container.

Creating a Data Pipeline

Now that we have our cluster and dataset set up, we can move on to creating our data pipeline.

  1. Navigate to your Azure Data Factory.
  2. Click “Author & Monitor.”
  3. Click “Author.”
  4. Click “New Pipeline.”
  5. Give your pipeline a name, such as “patient-data-pipeline.”
  6. Drag and drop the “Copy Data” activity into the pipeline.
  7. Configure the “Copy Data” activity as follows:
    • Source Dataset: “Excel”
    • File Path: “https://.blob.core.windows.net//“
    • Source format: “excel”
    • Sheet name or index: “Sheet1”
    • Use first row as headers: “Yes”
    • Allow schema drift: “Yes”
    • Validate schema: “No”
    • Sink Dataset: “Azure Blob Storage”
    • File Path: “https://.blob.core.windows.net//output”
    • Sink format: “parquet”
    • Partitioning mode: “Dynamic”
    • Maximum concurrency: “20”
    • Enable staging: “Yes”
  8. Click “Publish all.”

Running the Data Pipeline

Now that we have created our data pipeline, we can run it to see how it performs.

  1. Navigate to your Azure Data Factory.
  2. Click “Author & Monitor.”
  3. Click “Author.”
  4. Click “Add Trigger.”
  5. Choose a trigger type and configure it as desired, such as a one-time or recurring trigger.
  6. Click “Create.”

This will run the data pipeline and move the patient data from the Excel file to the output folder in our Azure storage account.

Analyzing the Data

Once the data pipeline has completed, we can use Azure Databricks to analyze and visualize the data.

  1. Navigate to your Azure Databricks workspace.
  2. Click the “Workspace” tab.
  3. Click “Import.”
  4. Import the “patient-data.parquet” file from the output folder in your Azure storage account.
  5. Create a new notebook in Azure Databricks.
  6. Use the following code to read the Parquet file and create a DataFrame:
df = spark.read.parquet("dbfs:/mnt/<container_name>/output/patient-data.parquet")
display(df)
  1. Run the code to display the DataFrame.

This will display the patient data from our input file in an interactive table that allows us to analyze and visualize the data.

Conclusion

Building a data pipeline with Azure Databricks is a straightforward process that can help streamline your data processing and analysis workflows. With Azure Databricks, you can easily create, run, and monitor your data pipelines, ensuring that your datasets are processed correctly and delivered to where they need to be.

By following the steps outlined in this tutorial, you can create your own data pipeline and start analyzing your data in no time.

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