Cloud Data Fusion is a fully managed, cloud-native data integration service that allows you to efficiently build and manage ETL/ELT data pipelines. It provides a graphical interface to design data pipelines, making it easier for both technical and non-technical users to create complex data workflows.

1. ETL/ELT: Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) are processes used to move data from source systems to a data warehouse or data lake.
2. Pipelines: A sequence of data processing steps, including data extraction, transformation, and loading.
3. Plugins: Reusable components that perform specific tasks within a pipeline, such as reading from a database or writing to a storage system.
4. Wrangling: The process of cleaning and transforming raw data into a more usable format.

• Visual Interface: Drag-and-drop interface for designing data pipelines.
• Pre-built Connectors: Connect to various data sources and sinks, including databases, cloud storage, and on-premises systems.
• Data Transformation: Built-in transformations and the ability to write custom transformations.
• Monitoring and Logging: Real-time monitoring and logging of pipeline executions.
• Scalability: Automatically scales to handle large volumes of data.

1. Go to the Google Cloud Console.
2. Navigate to the API & Services section.
3. Click on Enable APIs and Services.
4. Search for "Cloud Data Fusion" and enable the API.

1. In the Google Cloud Console, navigate to Cloud Data Fusion.
2. Click on Create Instance.
3. Fill in the required details:
   • Instance Name: A unique name for your instance.
   • Region: Select the region where you want to deploy the instance.
   • Zone: Optionally, select a specific zone.
4. Click Create to provision the instance.

1. Once the instance is created, click on the instance name to open the instance details page.
2. Click on Instance URL to open the Cloud Data Fusion UI.

1. Create a New Pipeline:

   • In the Cloud Data Fusion UI, click on Studio.
   • Click on Create Pipeline and select Data Pipeline.

2. Add a Source:

   • Drag the GCS (Google Cloud Storage) source plugin onto the canvas.
   • Configure the plugin by specifying the bucket and file path.

3. Add a Transformation:

   • Drag the Wrangler plugin onto the canvas.
   • Connect the GCS source to the Wrangler.
   • Configure the Wrangler to clean and transform the data as needed.

4. Add a Sink:

   • Drag the BigQuery sink plugin onto the canvas.
   • Connect the Wrangler to the BigQuery sink.
   • Configure the plugin by specifying the dataset and table.

5. Deploy and Run the Pipeline:

   • Click on Deploy to deploy the pipeline.
   • Once deployed, click on Run to execute the pipeline.

Below is a JSON representation of a simple pipeline that reads data from a GCS bucket, transforms it using Wrangler, and writes it to a BigQuery table.

1. Objective: Create a pipeline that reads a CSV file from a GCS bucket, transforms the data, and loads it into a BigQuery table.
2. Steps:
   • Create a new pipeline in Cloud Data Fusion.
   • Add a GCS source and configure it to read a CSV file.
   • Add a Wrangler transformation to clean and transform the data.
   • Add a BigQuery sink and configure it to write to a specific table.
   • Deploy and run the pipeline.

Follow the steps outlined in the "Creating a Simple Pipeline" section to complete the exercise.

• Incorrect File Path: Ensure the file path in the GCS source plugin is correct.
• Schema Mismatch: Ensure the schema in the Wrangler transformation matches the schema in the BigQuery table.
• Permissions: Ensure the Cloud Data Fusion service account has the necessary permissions to read from GCS and write to BigQuery.

In this section, you learned about Cloud Data Fusion, its key features, and how to set it up. You also created a simple pipeline to load data from Cloud Storage to BigQuery. This knowledge will help you build and manage complex data integration workflows on Google Cloud Platform. In the next module, we will explore more advanced data and analytics services on GCP.