Data Engineering: Azure Databricks Hands-On

Getting Started with Azure Databricks: Creating a Workspace and Configuring a Spark Cluster

Introduction: Azure Databricks is a powerful data analytics platform provided by Microsoft in collaboration with Databricks. It integrates with various Azure services and offers three primary environments for developing data-intensive applications: Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning.Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform provided by Microsoft Azure. In this blog post, we will walk through the process of creating an Azure Databricks workspace using the Azure portal and then demonstrate how to create and configure a Spark cluster within that workspace.

Prerequisites: Before we begin, make sure you have an Azure subscription and the necessary permissions to create resources within Azure. Additionally, a basic understanding of Apache Spark and its concepts will be helpful.

Please go through the video which covers below topics -

1. Azure Databricks Workspace: Azure Databricks is a cloud-based analytics platform that utilizes Apache Spark for processing big data and provides collaborative capabilities for data scientists, analysts, and engineers. The workspace is the environment where you can create and manage your Databricks resources, such as clusters, notebooks, and jobs.
2. Configure a Spark Cluster: Databricks is designed and developed to handle Big Data.In Azure Databricks, you can configure a Spark cluster to process your big data workloads. Within Databricks we can create Spark clusters which in the backend spin up a bunch of VM machines. A cluster is a set of virtual machines (VMs) that work together to run Spark jobs. You can customize the cluster’s size, instance types, and associated configurations to meet your specific requirements.
3. Creating Pool: Databricks offers a feature called “Pools” that allows users to create and manage a pool of machines for their workloads. By utilizing pools, users can preconfigure a set of clusters to be on standby, ready to execute jobs or tasks as needed. The main purpose of using pools is to enhance cluster creation time and improve job execution efficiency. Rather than creating a new cluster from scratch each time a job needs to run, Databricks can leverage the pre-configured clusters in the pool, which are already up and running. This significantly reduces the startup time since the clusters are already provisioned and ready to accept tasks.
4. Create Notebook: A Spark cluster notebook is a web-based interface that provides an interactive environment for running code and creating visualizations using different programming languages, such as Python, Scala, SQL, or R, depending on your preferences and requirements.
5. Import Notebook: You can import existing notebooks into your Azure Databricks workspace. This allows you to reuse and share code across multiple notebooks or collaborate with others by sharing your analysis or code snippets.
6. Schedule a Job: Azure Databricks allows you to schedule jobs to automate the execution of notebooks or Spark applications. You can specify the frequency and time at which the job should run, and Databricks will automatically trigger the execution, allowing you to perform regular data processing or updates without manual intervention.
7. Cleanup Resource: Cleaning up resources is an important aspect of managing your Azure Databricks workspace efficiently. When you no longer need a cluster, notebook, or other resources, it’s recommended to clean them up to avoid unnecessary costs and clutter in your workspace.

That’s it! You have learned how to create an Azure Databricks workspace, create and configure a Spark cluster, create a notebook, import a notebook, and schedule a job. You can now start leveraging Azure Databricks for your big data and analytics workloads. Please let me know if you have any specific questions about these topics, and I’ll be happy to provide further details or assistance based on my knowledge.