Microsoft Power BI Training | Azure Data Engineering Training

1. PySpark with ADLS | Azure Databricks Training PySpark, or Python with Apache Spark, can be used with Azure Data Lake Storage (ADLS) for processing large-scale data. - Microsoft Azure Online Data Engineering Training To use PySpark with ADLS, you need to follow these general steps: 1. Set Up Your Azure Environment: - Create an Azure account if you don't have one. - Set up an Azure Data Lake Storage account. - Note down your storage account name and key, which will be used for authentication. 2. Install Required Packages: - Install PySpark and the Azure Storage connector. You can use `pyspark` and `azure-storage` packages for this purpose. You can install them using pip: ```bash pip install pyspark pip install azure-storage ``` 3. Create a Spark Session: - In your PySpark script or Jupyter notebook, create a Spark session that includes the necessary configurations for connecting to ADLS. You'll need to provide your ADLS credentials. ```python from pyspark.sql import SparkSession spark = SparkSession.builder \ .appName("ADLS Example") \ .config("fs.azure.account.auth.type.<your-storage-account-name>.dfs.core.windows.net", "key") \

2. .config("fs.azure.account.key.<your-storage-account-name>.dfs.core.windows.net", "<your- storage-account-key>") \ .getOrCreate() ``` 4. Read and Write Data to ADLS: - Use PySpark's DataFrame API to read and write data to/from ADLS. ```python # Read data from ADLS into a DataFrame df = spark.read.csv("adl://<your-storage-account- name>.azuredatalakestore.net/path/to/your/data.csv", header=True, inferSchema=True) # Perform transformations or analyses on the DataFrame # Write the DataFrame back to ADLS df.write.csv("adl://<your-storage-account- name>.azuredatalakestore.net/path/to/output/data.csv", header=True) ``` - Adjust the file paths and formats according to your data and requirements. 5. Submit Your PySpark Job: - Depending on your setup, you might run your PySpark script locally or submit it to a Spark cluster. Ensure that the necessary dependencies are available in your environment. 6. Authentication: - For secure access, you might consider using Azure Active Directory (Azure AD) for authentication. This involves using service principals and Azure AD tokens. Update your Spark session configuration accordingly. - Azure Data Engineering Online Training Visualpath is the Leading and Best Institute for learning Azure Data Engineering Training. We provide Azure Databricks Training, you will get the best course at an affordable cost. Attend Free Demo Call on - +91-9989971070. Visit Our Blog: https://azuredatabricksonlinetraining.blogspot.com/ Visit:https://www.visualpath.in/azure-data-engineering-with- databricks-and-powerbi-training.html