Step-By-Step Guide To Creating a Pipeline in Databricks

Here is step by step guide to creating Pipeline in Azure Databricks.

Define a Notebook Task You Want To Run

To start creating a pipeline in Databricks, you define the tasks you want to include in your pipeline. These tasks will typically be notebooks that contain the code you want to execute. For example, you can create a new Python 3 notebook and write the code you provided in your question.

Open the Databricks UI and Navigate to Your Workspace

Once your notebooks are ready, open the Databricks User Interface (UI) and navigate to the workspace. It is where you manage and create your pipelines.

Click on “Pipelines” in the Sidebar Menu

In the Databricks sidebar menu, you will find a section called "Pipelines." Clicking on this will take you to the Pipeline management page.

Click on the “Create Pipeline” Button

On the Pipeline management page, locate the "Create Pipeline" button and click on it. It will initiate the pipeline creation process.

Enter a Name and Description for Your Pipeline

Give the pipeline a descriptive name that reflects its purpose, such as "Data Processing Pipeline." It's also helpful to describe what the pipeline does.

In the “Pipeline Definition” Section, Click on “Add New Task”

In the Pipeline Definition section, you need to specify the tasks that make up your pipeline. Click on the "Add New Task" button to define a new task for your pipeline.

Select “Notebook Task” as the Task Type

In the task creation dialog, select "Notebook Task" as the task type. It tells Databricks that you will execute a notebook as part of your pipeline.

Choose the Notebook That Contains Your Code

To select the notebook of your code, click over the "Select Notebook" button in the task creation dialog. It will open a file browser that allows you to locate and choose the appropriate notebook (e.g., "2023-07-07-file-2.ipynb").

Specify Any Input and Output Parameters for Your Notebook Task

If your notebook requires any input parameters, such as variables or arguments, you can define them in this step. Input parameters allow you to pass values to your notebook at runtime. Similarly, you can state output parameters if your notebook produces any results to capture.

Review the Settings and Click Over the “Create” Button

Before finalizing your pipeline, review all the settings and configurations you have made so far. Ensure the notebook task is selected correctly and define input/output parameters properly. Once you are good, click over the "Create" button to create your pipeline.

Run Your Pipeline by Clicking on the “Run Now” Button

After creating the pipeline, you can start execution by clicking on the "Run Now" button. It will trigger the execution of the notebook task defined in your pipeline.

Monitor the Progress of Your Pipeline in the Databricks UI

As your pipeline runs, you can monitor its progress and status in the Databricks UI. You can view the status of each task, check for any logs or errors generated during execution, and track the overall progress of the pipeline.

By following these steps, you can create a pipeline in Databricks and automate the execution of your code. Pipelines allow you to define complex workflows, chain multiple tasks as one, and schedule their execution at specific intervals. It helps streamline and manage your data processing pipelines efficiently.