Consume data from a data asset in a job

To consume data from a data asset in a job related to designing and implementing a data science solution on Azure, you can use various Azure services and tools. In this article, we will explore how to leverage Azure Data Factory and Azure Databricks to perform this task.

Azure Data Factory

Azure Data Factory is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and data transformation. With Azure Data Factory, you can easily create pipelines to move and transform data from various sources.

Azure Databricks

Azure Databricks is an Apache Spark-based analytics platform that provides a collaborative environment for building data pipelines and performing advanced analytics. It simplifies the process of data ingestion, processing, and analytics by providing a unified workspace.

Understanding Data Assets

In the context of this article, a data asset refers to a specific data source or dataset that you want to consume within your data science solution. This data asset could be stored in various formats, such as Azure Blob storage, Azure Data Lake Storage, Azure SQL Database, or any other supported data sources.

Steps to Consume Data from a Data Asset

Step 1: Create an Azure Data Factory pipeline

1. Create an Azure Data Factory instance in the Azure portal.
2. Open the Azure Data Factory interface and click on the “Author & Monitor” button.
3. In the authoring interface, click on the “Author” tab and create a new pipeline.
4. Add an “Azure Blob Storage Source” activity to the pipeline. Configure the source to point to the location of your data asset.
5. Optionally, you can add transformation activities or other data processing tasks within the pipeline.

Step 2: Configure Azure Databricks to consume data

1. Create an Azure Databricks workspace in the Azure portal.
2. Open the Databricks workspace and create a new notebook.
3. Within the notebook, you can use Apache Spark APIs to read data from the data asset. For example, to read data from Azure Blob storage, you can use the following code:

python
# Import required libraries
from pyspark.sql import SparkSession

# Create Spark session
spark = SparkSession.builder.getOrCreate()

# Read data from Azure Blob storage
df = spark.read.format(“csv”).load(“abfss://.dfs.core.windows.net//“)

4. Once you have consumed the data using Spark APIs, you can perform data transformations, feature engineering, or any other data science tasks within the notebook.

Step 3: Integrate Azure Data Factory with Azure Databricks

1. In the Azure Data Factory authoring interface, add an “HDInsightSpark” activity to the pipeline.
2. Configure the HDInsightSpark activity to point to your Azure Databricks workspace and specify the notebook path.
3. Optionally, you can pass parameters or arguments to the notebook using the “Pass input data as parameter” feature in the HDInsightSpark activity.

Step 4: Trigger and monitor the pipeline

1. Save and publish the Azure Data Factory pipeline.
2. In the Azure Data Factory interface, click on the “Monitor & Manage” button.
3. Trigger the pipeline manually or schedule it to run at specific intervals.
4. Monitor the pipeline execution and check the logs for any errors or warnings.

By following these steps, you can consume data from a data asset in a job related to designing and implementing a data science solution on Azure. Azure Data Factory and Azure Databricks provide a powerful combination for orchestrating and processing data, allowing you to build scalable and efficient data science pipelines.

Remember to refer to the Microsoft documentation for detailed information and additional features of Azure Data Factory and Azure Databricks. Happy data consuming!