Azure Databricks | Azure Analytics

Introduction to Azure Databricks

Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. It provides a fast, easy, and collaborative Apache Spark-based analytics service. Azure Databricks is designed to make big data and AI simple for data scientists, data engineers, and business analysts.

Setting Up Azure Databricks

To get started with Azure Databricks, you need to create an Azure Databricks workspace in your Azure portal.

1. Sign in to the Azure portal.

2. In the left-hand navigation pane, click Create a resource.

3. In the Search the Marketplace box, enter "Azure Databricks" and then click Azure Databricks in the results.

4. Click Create to open the Create Azure Databricks workspace page.

5. Follow the prompts to create the workspace.

Creating a Cluster

Once you have created your Azure Databricks workspace, the next step is to create a cluster. A cluster is a set of computation resources and configurations on which you run data engineering, data science, and data analytics workloads.

1. In the Azure Databricks workspace, click Clusters in the left-hand navigation.

2. Click the Create Cluster button.

3. Enter the cluster name and configure the cluster settings.

4. Click Create Cluster.

Creating a Notebook

Notebooks are a common way to develop and share data engineering and data science projects. In Azure Databricks, you can create notebooks to run your code and visualize data results.

1. In the Azure Databricks workspace, click Workspace in the left-hand navigation.

2. Click the drop-down arrow next to your user name, and select Create > Notebook.

3. Name your notebook and select the default language (Python, Scala, SQL, or R).

4. Click Create.

Running a Simple Apache Spark Job

Let's run a simple Apache Spark job in the notebook to get a feel for how Azure Databricks works.

1. In the notebook, enter the following Python code:

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("SimpleApp").getOrCreate()
data = [("Alice", 1), ("Bob", 2), ("Cathy", 3)]
df = spark.createDataFrame(data, ["Name", "Value"])
df.show()

2. Click the Run button to execute the code.

3. The output should display a table with the data.

+-----+-----+
| Name|Value|
+-----+-----+
|Alice|    1|
|  Bob|    2|
|Cathy|    3|
+-----+-----+

Integration with Azure Data Lake Storage

Azure Databricks can easily integrate with Azure Data Lake Storage (ADLS) to read and write data. Below is an example of how to read data from ADLS.

1. Mount your ADLS account to Databricks:

configs = {"fs.azure.account.key..blob.core.windows.net": ""}
dbutils.fs.mount(source = "wasbs://@.blob.core.windows.net/",
                 mount_point = "/mnt/",
                 extra_configs = configs)

2. Read data from the mounted ADLS location:

df = spark.read.csv("/mnt//path/to/your/csvfile.csv")
df.show()

Conclusion

Azure Databricks is a powerful platform for big data processing and machine learning. By following this tutorial, you should now have a basic understanding of how to set up and use Azure Databricks for your data analysis tasks. Explore further to unlock its full potential.

Azure Databricks Tutorial