Azure Databricks Deployment Strategies
Introduction:
Azure Databricks is a cloud-based big data analytics and machine learning platform provided by Microsoft Azure. Databricks was designed to unify data science, data engineering, and business data analytics on Spark by creating an easy to use environment that enables users to spend more time working effectively with data, and less time focused on managing clusters and infrastructure.
Azure Databricks is a unified, open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale. The Azure Databricks Lakehouse Platform integrates with cloud storage and security in your cloud account, and manages and deploys cloud infrastructure on your behalf.
What is Azure Databricks used for?
Azure Databricks empowers users to perform a wide range of operations on their datasets, including processing, storage, data cleansing, collaboration, analysis, modeling, and monetization. It can be used to build and deploy data engineering workflows, machine learning models, analytics dashboards,Data processing workflows,Machine learning (ML) modeling, tracking, Generating dashboards and visualizations, Data discovery and Data ingestion.
Key features of Azure Databricks:
Key features and components of Azure Databricks include:
- Unified Analytics Platform: Azure Databricks brings together data engineering and data science on a single platform, allowing teams to collaborate seamlessly.
- Apache Spark: Databricks is built on Apache Spark, an open-source, distributed data processing framework, making it suitable for processing large datasets and performing distributed data analytics.
- Data Ingestion: It supports various data sources, including Azure Data Lake Storage, Azure Blob Storage, and more, for efficient data ingestion.
- Data Transformation: Databricks allows users to clean, transform, and manipulate data using languages like Python, Scala, and SQL.
- Machine Learning: It includes libraries for machine learning, enabling data scientists to build, train, and deploy machine learning models.
- Integration: Azure Databricks can be integrated with various Azure services like Azure Synapse Analytics, Azure Data Factory, and Azure DevOps for comprehensive data analytics and data engineering solutions.
Deployment Methods:
We can use Azure Databricks by creating Databricks workspace in our Azure subscription. You can either manually deploy it from the azure portal but following deployment methods can also be used:
- Using the Azure portal user interface.
- Using an Azure Resource Manager (ARM) or Bicep template.
- Using the New-AzDatabricksWorkspace Azure PowerShell cmdlet
- Using the az databricks workspace create Azure command line interface (CLI) command.
Databricks Deployment Using Azure Portal:
- Go to and search for Azure Databricks.
- Click on create and fill all the required fields.
Pricing Tiers of Databricks:
When you create a workspace, you must specify one of the following pricing tiers:
- Standard - Core Apache Spark capabilities with Microsoft Entra integration.
- Premium - Role-based access controls and other enterprise-level features.
- Trial - A 14-day free trial of a premium-level workspace
- After adding all fields, click on Review + Create.
- Click on “Go to Resource” and after that click on Launch workspace.
5- Databricks workspace is created, now you can implement notebooks and other ML tasks.
Databricks Deployment Using Azure Powershell:
Before deploying databricks using powershell, make sure that you have imported the Az.Databricks module. Run following commands to validate it.
- Import-Module Az.Databricks
- get-module Az.Databricks
I have used Azure Powershell to deploy it and here are the parameters needed to deploy it through powershell:
New-AzDatabricksWorkspace-Name DB_Demo
-ResourceGroupName Azure-Databricks
-Location westeurope
-ManagedResourceGroupName AdminDB
-Sku Standard
Let's break down the parameters:
- ResourceGroupName: Name of Azure Resource Group where the Databricks workspace will be created.
- Name: Specifies the name of the Databricks workspace.
- Location: Specifies the Azure region where the Databricks workspace will be located.
- Sku: Specifies the SKU (Service Level Agreement) of the Databricks workspace.
- ManagedResourceGroupName (Optional): Specifies the name of the resource group where the managed resources will be create
- We can also deploy through Azure CLI and (ARM) or Bicep template but i have used two methods to deploy databricks which i mentioned above.
Connection between Azure Databricks and Azure Sentinel:
We can connect Azure Databricks and Azure Sentinel to perform activities like:
- Ingesting data from various sources into Azure Sentinel for analysis.
- Performing ETL operations on data before sending it to Azure Sentinel.
- Creating custom machine learning models for data analysis in Azure Sentinel.
- Deployment of infrastructure using CI/CD pipelines.
To use Azure Databricks in Azure Sentinel, you can follow the steps below:
- Create a new Azure Databricks workspace.
- Create a new cluster in the workspace.
- Install the Azure Sentinel connector on the cluster.
- Configure the connector to send data to Azure Sentinel.
In my upcoming blog, I'll delve into the extensive capabilities of Azure Databricks when integrated with Microsoft Sentinel. I will showcase the versatile machine learning module, which allows you to craft notebooks, execute incident responses leveraging machine learning, and explore the manifold functionalities of Azure Databricks. We'll delve into the "Bring Your Own ML" (BYO ML) package, which incorporates Microsoft's cutting-edge research and best practices at the forefront of security-oriented machine learning. This package provides a comprehensive toolkit, encompassing utilities, pre-configured notebooks, and algorithm templates tailored to address a spectrum of security challenges. Stay tuned for a deep dive into this powerful combination of technologies!