Starting a Databricks project with Python is a powerful way to build data-driven applications using Azure Databricks and Apache Spark. This guide walks through the essential steps to get started, covering everything from setting up your environment to writing your first Python code and connecting to Azure data sources.
Step 1: Setting Up Your Azure Databricks Workspace
Create a Databricks Workspace
- In the Azure portal, search for “Databricks” and select “Azure Databricks.”
- Click “Create,” select your subscription, and create a resource group if you don’t have one.
- Choose a name for your workspace, select your region, and hit “Review + Create.”
Create and Configure a Cluster
- Navigate to the Databricks workspace, select “Clusters” from the sidebar, and click “Create Cluster.”
- Configure your cluster by choosing a runtime version (e.g., “Databricks Runtime 12.1 for ML”) and setting worker node types.
- Click “Create Cluster” to start the cluster.
Install Necessary Libraries
- In your Databricks workspace, go to “Libraries” under your cluster, and install required libraries such as
pandas,numpy, or any other Spark libraries necessary for your project.
- In your Databricks workspace, go to “Libraries” under your cluster, and install required libraries such as
Step 2: Creating Your First Notebook
Create a Python Notebook
- Click on “Workspace” > “Create” > “Notebook.”
- Name your notebook and select “Python” as the default language.
- Attach your notebook to the running cluster.
Write and Run Python Code
- Databricks supports Python 3, and you can use popular libraries like
pandas,numpy, andpyspark. - Example code to start loading and exploring a CSV file:
- Databricks supports Python 3, and you can use popular libraries like
# Import SparkSession
from pyspark.sql import SparkSession
# Create a Spark session
spark = SparkSession.builder.appName("DatabricksProject").getOrCreate()
# Read a CSV file into a DataFrame
df = spark.read.format("csv").option("header", "true").load("/databricks-datasets/samples/population-vs-price/data_geo.csv")
# Display the DataFrame
df.show()
Python
This code snippet demonstrates how to use the SparkSession in Databricks to read and display a CSV file stored in the Databricks dataset directory. The .show() method shows the first few rows of the dataset.
Step 3: Connecting to Azure Data Sources
Databricks integrates seamlessly with Azure Data Lake, Blob Storage, and other Azure services. Here’s how to connect your project to Azure Blob Storage:
- Mount Azure Blob Storage
- To access data stored in Azure Blob Storage, you need to mount the storage in your Databricks workspace.
- Go to your notebook and run:
dbutils.fs.mount(
source = "wasbs://[your-container]@[your-storage-account].blob.core.windows.net",
mount_point = "/mnt/blob-storage",
extra_configs = {"" :dbutils.secrets.get(scope = "my-scope", key = "my-key")}
)
Python
This code mounts your Azure Blob Storage container to the /mnt/blob-storage directory within Databricks. Make sure to set up your secrets in Azure Key Vault for security purposes.
- Read Data from Mounted Storage
# Read data from Azure Blob Storage
df_blob = spark.read.format("csv").option("header", "true").load("/mnt/blob-storage/my-data.csv")
# Display the data
df_blob.show()
Python
This code snippet loads a CSV file directly from your mounted Azure Blob Storage location.
Step 4: Data Processing and Analysis Using PySpark
PySpark is the Python API for Apache Spark, and it’s perfect for big data processing in Databricks. Here are some common tasks:
- Data Transformation
# Select specific columns and filter rows
df_filtered = df_blob.select("column1", "column2").filter(df_blob["column1"] > 100)
# Group by and aggregate data
df_grouped = df_blob.groupBy("column2").agg({"column1": "mean"})
df_grouped.show()
Python
- Writing Processed Data to Azure Data Lake
df_filtered.write.mode("overwrite").parquet("/mnt/blob-storage/processed-data/")
Python
Conclusion
By following these steps, you can effectively set up and start a Databricks project using Python, taking advantage of Databricks’ capabilities for big data processing and integration with Azure services. For more details, you can always refer to the official Databricks documentation.
This blog combines practical steps, code examples, and best practices to provide a comprehensive guide for new users starting a Databricks project with Python.