How Databricks Integrates with AWS, Azure & Google Cloud

 Introduction 

If you’re learning about data tools, you might have heard of Databricks. It’s a platform that helps companies work with big data, build machine learning models, and run smart analytics. But where does Databricks actually run? 

The answer: in the cloud. 

That means it doesn’t run on your laptop or local server. Instead, it runs on popular cloud services like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). 

In this blog, we’ll explain how Databricks works with each of these cloud platforms in simple terms. Whether you're a beginner or just curious, you’ll get a clear picture of how Databricks connects with the cloud to make data work easier. 

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Agenda 

  • What Is the Cloud and Why Does Databricks Use It? 
  • Databricks on AWS 
  • Databricks on Azure 
  • Databricks on Google Cloud 
  • Choosing the Right Cloud for Your Needs 
  • Conclusion 

 

1. What Is the Cloud and Why Does Databricks Use It? 

Before we dive in, let’s understand what the “cloud” means. The cloud is just a way of storing and processing data on the internet instead of on your own computer. 

So instead of running heavy programs and storing huge files on your machine, cloud platforms like AWS, Azure, and Google Cloud provide powerful computers (called servers) that you can use remotely. 

Databricks uses the cloud to give users an easy way to store data, run code, and collaborate on projects without needing to manage the hardware or software themselves. It’s faster, safer, and more scalable. 

 

2. Databricks on AWS 

Amazon Web Services (AWS) was the first cloud to support Databricks. In fact, Databricks originally started on AWS. 

Here’s how it works: 

  • Easy Data Storage: Databricks connects directly to Amazon S3 (a storage system) to save and read data quickly. 
  • Powerful Processing: It uses EC2 (virtual computers from AWS) to run your code and handle big workloads. 
  • Built-in Security: It follows AWS security rules to keep your data safe. 
  • Auto-scaling: If you need more computing power, Databricks on AWS can automatically increase resources. 

This setup is great for teams already using AWS for other services. Everything stays in one place, making it easier to manage. 

 

3. Databricks on Azure 

Microsoft Azure is the only cloud that offers Azure Databricks as a fully managed, first-party service. That means it's deeply integrated into Azure, making the user experience smoother for Microsoft users. 

Here’s what’s special: 

  • Direct integration with Azure tools like Azure Data Lake, Power BI, and Azure Synapse. 
  • One-click setup using your existing Microsoft account. 
  • Easy dashboarding with tools like Power BI for data visualization. 
  • Azure Active Directory support for user access and login management. 

If you’re already using Microsoft Office, Teams, or other Azure services, Azure Databricks will feel like a natural fit. 

 

4. Databricks on Google Cloud 

Google Cloud Platform (GCP) added Databricks support more recently. But it brings some strong features for modern data teams. 

Here’s how Databricks fits with Google Cloud: 

  • Storage with Google Cloud Storage (GCS) – stores large data files securely. 
  • Data integration with BigQuery – allows fast querying of massive datasets. 
  • Built-in AI tools – Google Cloud’s AI services can work with Databricks models. 
  • Fast setup – use your Google account to get started quickly. 

This setup is great for companies that already use Google tools like Gmail, Google Drive, or Google Workspace. 

 

5. Choosing the Right Cloud for Your Needs 

So which cloud should you pick for using Databricks? 

It depends on what your company or team already uses. Here’s a simple guide: 

  • Use AWS Databricks if your team is already using AWS and needs powerful, flexible infrastructure. 
  • Use Azure Databricks if you're working with Microsoft products and want tight integration. 
  • Use Databricks on Google Cloud if your team likes Google tools and plans to work with AI and big data projects. 

No matter which cloud you choose, the Databricks interface and core features stay the same. You’ll still get access to notebooks, machine learning tools, and data engineering workflows. 

 

Conclusion 

Databricks is a powerful platform for working with data and AI—and it becomes even more powerful when combined with cloud platforms like AWS, Azure, and Google Cloud. 

Each cloud service brings its own strengths. AWS offers flexibility and scale, Azure brings tight Microsoft integration, and Google Cloud connects well with AI tools. 

The best part? You don’t have to learn a new tool every time. Databricks looks and works similarly across all clouds. That means you can focus on learning data science or building machine learning models—without worrying about the technical setup. 

Whether you're a beginner, a student, or a team planning to adopt cloud tools, understanding how Databricks works with these platforms is a great step toward working smarter with data. 

What’s Next? Build Smarter with Databricks in the Real World 

Join our hands-on sessions at AccentFuture and see how companies across industries like e-commerce, finance, and healthcare are using Databricks to solve real-world problems — from predictive maintenance and customer behavior insights to fraud detection and dynamic pricing. 

Learn how Databricks works with popular sources like Amazon S3, Azure Data Lake, Google Cloud Storage, and traditional systems to power seamless data pipelines, machine learning workflows, and real-time dashboards — all in a secure, scalable, and collaborative cloud environment. 

✅ Connect it. ✅ Process it. ✅ Make smarter decisions. 
 Step into the future of unified analytics with Databricks + Cloud at AccentFuture. 

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