Azure Databricks Modern Analytics Architecture Overview

Azure Databricks is a fast, easy, and collaborative analytics platform that is optimized for big data and AI workloads. It's built on top of the Apache Spark engine.

With Azure Databricks, you can work with large-scale datasets and get faster results than with traditional analytics tools. This is because Azure Databricks is optimized for performance and scalability.

Azure Databricks provides a range of features and tools that make it easy to work with data, including notebooks, clusters, and libraries. These tools allow you to collaborate with others and share your work easily.

Azure Databricks is a fully managed Spark cluster that processes large streams of data from multiple sources. It's a game-changer for handling big data and complex analytics workloads.

Azure Databricks cleans and transforms structureless data sets, making it a powerful tool for data engineering and machine learning tasks. With its ability to combine processed data with structured data from operational databases or data warehouses, Azure Databricks provides a unified environment for teams to work collaboratively.

Here are some core components of Azure Databricks:

These components work together to provide a scalable and secure data lake for high-performance analytics workloads. With Azure Databricks, you can build, deploy, and manage predictive analytics solutions, and even visualize data in dashboards.

To get the most out of learning about Core Components, you should start by understanding the key features of Azure Databricks. It's designed to help you process large datasets efficiently.

Azure Databricks is particularly useful for building ETL pipelines. ETL, or Extract, Transform, Load, is a process that helps you organize and prepare data for analysis.

To explore Azure Databricks, you'll want to analyze its benefits. This includes understanding how it can help you handle large datasets with ease.

Here are some key benefits of using Azure Databricks:

Delving into practical use cases will also help you understand the value of Azure Databricks. By learning how to build ETL pipelines, you'll be able to prepare your data for analysis and make informed decisions.

Azure Databricks is a powerful data analytics platform that offers a range of key features to help you process large datasets efficiently. Its collaborative workspace allows users to share notebooks, data, and insights with team members, making it easy to collaborate on data engineering and machine learning tasks.

Azure Databricks provides a scalable and reliable platform built on top of Apache Spark, which can handle large datasets and complex workflows. It offers automation features that simplify creating, managing, and deploying big data processing and machine learning workloads.

The platform integrates tightly with the Microsoft Azure cloud, making it easy to integrate with other Azure services such as Azure Blob Storage, Azure Data Lake Storage, and Azure SQL Database. This integration enables seamless data processing and analytics workflows.

Azure Databricks also provides a unified data engineering, data science, and analytics environment, allowing teams to work collaboratively and seamlessly across different tasks and projects. Its scalable analytics capabilities make it ideal for handling big data and complex analytics workloads.

Here are some of the key features of Azure Databricks:

A SQL Compute Cluster is a powerful tool for processing and analyzing large datasets. It's a set of nodes that work together to run jobs and provide automated cluster provisioning.

You can choose from various cluster sizes, ranging from 2X-Small to 4X-Large. Each size corresponds to a specific number of DBUs (Database Units), which determine the cluster's processing power.

Here's a breakdown of the different cluster sizes and their corresponding DBU counts:

The cost of a SQL Compute Cluster varies depending on the cluster size and the number of VMs (Virtual Machines) you choose. The prices are listed in the table, but keep in mind that they're currently empty, so you'll need to check the latest pricing information.

Data Management is a crucial step in working with Azure Databricks. It's designed to help customers accelerate innovation by enabling data science with a high-performance analytics platform.

Azure Databricks combines the best of Databricks and Azure, featuring out-of-the-box Azure Active Directory integration, native data connectors, integrated billing, and compliance. This integration makes it easy to manage data and collaborate with others.

One-click set up and streamlined workflows enable data scientists, engineers, and business analysts to work together seamlessly. This collaboration is a game-changer for teams working on complex data projects.

Once you have imported data into the workspace, you can perform data engineering and exploration tasks with ease. Powerful tools make it simple to perform data transformations, cleaning, and visualization tasks.

Azure Databricks offers a range of security features to protect your data. Enhanced Security & Compliance Add-on is available for customers processing regulated data.

The Enhanced Security and Compliance offering includes Enhanced Security Monitoring and Compliance Security Profile. The Compliance Security Profile is required for PCI-DSS and recommended for HIPAA.

This add-on is only available on Azure Databricks Premium Tier workspace and is offered at 10% of list price added to the Azure Databricks product spend in a selected workspace.

The Enhanced Security & Compliance Add-on is a valuable feature for customers processing regulated data. It's offered at 10% of the list price added to the Azure Databricks product spend in a selected workspace.

This add-on is only available on Azure Databricks Premium Tier workspace, which is a key requirement.

Enhanced Security Monitoring is one of the capabilities included in the Enhanced Security & Compliance Add-on. It's available for customers to use.

Compliance Security Profile is another important feature, required for PCI-DSS and recommended for HIPAA.

Azure Databricks has a Service Level Agreement (SLA) that ensures high availability and reliability of its services. Reviewing this SLA is crucial for understanding the commitment Azure Databricks makes to its customers.

The SLA guarantees a minimum uptime of 99.9% for Azure Databricks, which translates to a maximum of 43 minutes of downtime per month. This ensures that your data and applications are always accessible.

Azure Databricks also provides a 99.99% uptime guarantee for its Spark clusters, which means that downtime is limited to just 4.38 minutes per month. This level of reliability is essential for businesses that rely heavily on data analytics.

Reviewing the SLA for Azure Databricks is a straightforward process that can be done online. It's an important step in ensuring that you understand your service commitments and can plan accordingly.

Azure Databricks bills you for virtual machines (VMs) provisioned in clusters and Databricks Units (DBUs) based on the VM instance selected.

You can get up to 37% savings over pay-as-you-go DBU prices by pre-purchasing Azure Databricks Units (DBU) as Databricks Commit Units (DBCU) for either 1 or 3 years.

Azure Databricks charges you for DBUs based on per-second usage, with the consumption depending on the type and size of the instance running Databricks.

Here's a breakdown of the pricing for different workloads:

The standard tier features of Azure Databricks are designed to provide a solid foundation for your data engineering and machine learning workloads. You can run interactive workloads to analyze data collaboratively with notebooks, and automated workloads to run fast and robust jobs via API or UI.

Interactive workloads allow you to collaborate in real-time, which is a game-changer for teams working on complex projects. With notebooks, you can share data and insights with your team members and work together seamlessly.

Apache Spark on Databricks platform is available across all standard tier features, including All-Purpose Compute, Jobs Compute, and Jobs Light Compute. This means you can leverage the power of Apache Spark to process large datasets and complex workflows.

Here's a breakdown of the standard tier features across each compute tier:

The premium tier features of Azure Databricks offer a range of advanced capabilities that can help you get the most out of your data analytics and machine learning workloads.

Interactive workloads are available in the All-Purpose Compute tier, allowing you to analyze data collaboratively with notebooks. This feature is also available in the Jobs Compute and Jobs Light Compute tiers.

The All-Purpose Compute tier includes standard features, as do the Jobs Compute and Jobs Light Compute tiers.

Role-based access control is available across all three premium tiers, enabling you to manage access to notebooks, clusters, jobs, and tables with precision.

Azure AD credential passthrough is available in the All-Purpose Compute and Jobs Compute tiers, but not in the Jobs Light Compute tier.

Conditional Authentication is available in the All-Purpose Compute tier, but not in the Jobs Compute or Jobs Light Compute tiers.

Cluster Policies, IP Access List, and Token Management API are all available in the All-Purpose Compute and Jobs Compute tiers, but only Cluster Policies is available in the Jobs Light Compute tier.

Here's a summary of the premium tier features by tier:

You can get up to 37% savings over pay-as-you-go DBU prices when you pre-purchase Azure Databricks Units (DBU) as Databricks Commit Units (DBCU) for either 1 or 3 years.

Azure Databricks bills you for virtual machines (VMs) provisioned in clusters and Databricks Units (DBUs) based on the VM instance selected. A DBU is a unit of processing capability, billed on a per-second usage. The DBU consumption depends on the size and type of instance running Azure Databricks.

Pay-as-you-go pricing for Azure Databricks is based on virtual machines (VMs) and Databricks Units (DBUs). DBU is a unit of processing facility, billed on per-second usage, and DBU consumption depends on the type and size of the instance running Databricks.

Azure Databricks offers a range of pricing options, including pre-purchase plans and pay-as-you-go pricing. You can pre-purchase Azure Databricks Units (DBU) as Databricks Commit Units (DBCU) for either 1 or 3 years to get up to 37% savings over pay-as-you-go DBU prices.

Here are the details of the pay-as-you-go pricing for Azure Databricks:

Note that the prices are subject to change and may vary by region. It's always a good idea to review the documentation for the most up-to-date pricing information.