Introduction to Azure Databricks Architecture And How to Use it Effectively

Pohan Lin
Published 11/03/2022
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Intro to Azure DatabricksBig data has become the main driver of insight across many industries. All that data isn’t much use without a way to analyze it, though. This has led to developing frameworks like Apache Spark to handle the load.

Those frameworks need to be managed and made accessible to data analysts. As such, management platforms like Databricks for industry have emerged. Essentially, this allows data specialists to work with multiple instances of Spark across cloud services like Azure. It might sound complex if you have never come across Databricks or Spark. This article will cover what Azure Databricks does and how you can use it for your big data needs.


What Is Apache Spark?

First, we need to talk about Apache Spark. It’s this framework that underpins Databricks’ primary functions. Spark is an open-source cluster computing solution. This means it uses networks of computers to perform simultaneous processing of large datasets.

Spark does this all “in-memory” meaning it uses the RAM of the networked machines as opposed to reading/writing to a disk. This framework is a highly efficient big data processing solution, but it needs a management layer for ease of use. That’s where Databricks comes in.



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What Is Azure Databricks?

Databricks is a data analytics program that acts as a management layer for Spark. Azure Databricks is optimized for use with Microsoft’s Azure cloud platform. In short, Azure Databricks uses cluster computing to unify data functions across the Azure platform.

Azure Databricks integrated management

The Azure version of Databricks runs optimized Spark APIs. It uses the computing power of the Azure cloud network for cluster processing. On top of this, it integrates functions from across the Azure platform, such as Data Lake Storage, Power Bi, and Azure Machine Learning.


Why Use Azure Databricks?

The integration of Azure services and support for multiple programming languages has made Azure Databricks a popular choice. It’s a highly versatile solution that supports Scala, R, SQL, and Python.


Collaborative Platform

The Databricks Workspace allows data specialists to work with shared dashboards and notepads. Quickly share insights and analysis models to improve data workflows, build new ideas, and optimize data analyst training.


Optimized Runtime Applications

As well as the optimized Spark APIs, Databricks Runtime also includes performance and security optimizations for all components. These are regularly updated with new versions. The dashboard lets you auto-scale processing tasks, among other quality-of-life functions.



The integrated functions of Azure Databricks make it an all-in-one solution for data analytics and machine learning. Your data lake can be managed and expanded with Azure blob storage, Azure data factory, etc. Your analytics can be fed into Power Bi and machine learning pipelines.

Insights can be easily pushed to the management layer. Integrated security protocols manage directories and sign-on. The end-to-end applications make Azure and Databricks an ideal business solution.



Databricks Components Explained

These are the core components that make up the Databricks platform.


Managed Clusters

It is the function that powers your processing. The cluster shares the workload to complete the processing task quickly. With Azure Databricks, you can set up a cluster with a few clicks.

It allows for on-demand processing. You can establish automated job groups to create a cluster for specific tasks. These groups automatically start up and shut down, ensuring that processing costs are kept to a minimum.


Spark & Delta

As mentioned above, Spark is the engine that processes your data in memory. Delta is an open-source file format that was designed to address the limitations of traditional data categorization.

Working together, these two open-source components optimize data sorting and processing. It gives Databricks the processing speed required for big data workflows.


ML Flow

The ML Flow open-source machine learning framework is the backend of Databricks’ ML workflow. ML Flow itself, is made up of the components you can see in the flowchart below.

ml flow components

Using the collaborative workspace in Databricks, ML developers can track and run projects. They can execute ML runs as jobs in Databricks and run in engine tests as seamless dashboard functions.


SQL Endpoints

SQL analytics in Databricks is powered by SQL endpoints. These are Spark clusters optimized for SQL processing. SQL analysts can access an SQL dashboard by switching views in the main Databricks UI.

It lets SQL specialists run queries against your data lake and share work on SQL dashboards. The integration of business intelligence tools allows you to access these endpoints through Power BI, Tableau, and others.


Use Cases for Azure Databricks

Databricks isn’t a catch-all solution for every business scenario. These are the best use cases for Azure Databricks. If your business fits into one of these descriptions, then it might be the solution for you.


Database & Mainframe Modernization

Data storage, collection, and processing are incredibly important in modern business. If you’re looking to modernize your data lakes or looking into mainframe modernization applications, then Azure Databricks has the integrations you need.


Machine Learning Production Pipeline

Using the underlying power of ML Flow, Databricks is a good choice if you need to get machine learning applications into production. Getting data science out of development and into production is a common problem, and Databricks can help streamline that workflow.


Big Data Processing

Azure Databricks is one of the most cost-effective options for big data processing. In terms of performance vs. cost, it offers high efficiency. If your business needs the best performance for on-demand data processing, then Databricks will likely be your best choice.


Business Intelligence Integration

Integrating Business Intelligence tools means you can open your data lake to analysts and engineers more easily. There’s no need for the creation of new pipelines when analysts need access to new data.

The data can be shared through SQL analytics, Power BI, and Tableau. If this is a bottleneck for your business, then Databricks will help enable your Business intelligence teams.


Final Thoughts

Data science and data technology advance quickly. While some businesses are still struggling with questions like what is IVR, others are using cloud computing and big data analysis to optimize their operations.

Modernization can be an intimidating process for businesses with established infrastructure. Yet, programs like Azure Databricks are making it easier to modernize legacy systems. We hope this guide explains whether Databricks is the best choice for your modernization.


About the Writer

Pohan LinPohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in analytics machine learning, web marketing, online SaaS business, and ecommerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing. Pohan Lin also published articles for domains such as PingPlotter.