In today’s data-driven world, organizations rely on Big Data platforms to build, deploy, share, and maintain enterprise-grade data, analytics, and AI solutions at scale. This article will explore how to Get started with Databricks, the leading platform in the industry. We will begin with a brief overview of what Databricks is.
What is Databricks?
Databricks is a unified, open analytics platform for building, deploying,
sharing, and maintaining enterprise-grade data, analytics, and AI solutions
at scale. The Databricks Data Intelligence Platform integrates with cloud
storage and security in your cloud account, and manages and deploys cloud
infrastructure for you.
To get started with Databricks, you need to set up an account and create a workspace. A workspace is a collaborative environment where you can create and manage notebooks, jobs, and other resources.
Here are three common ways to begin using Databricks:
Databricks Community Edition: A free version of Databricks ideal for learning and exploring Apache Spark. It includes a micro-cluster and notebook support but lacks key features like Delta Lake, MLflow, and Unity Catalog. Best suited for individual experimentation—not for production use.
Try Databricks: Also known as Express
Setup, this
option provides a free trial of Databricks using only your email. You’ll
receive Databricks usage credits to explore the platform’s features. This is
excellent for testing and evaluating Databricks, but it does have limitations,
such as restricted serverless compute resources.
Databricks on Your Cloud: This involves deploying a paid version of
Databricks within your own cloud account (AWS, Azure, or Google Cloud), making
it suitable for production workloads and large-scale data processing. Databricks
also offers a 14-day free
trial for users
who want to evaluate this option.
If you are new to Databricks, we recommend starting with the Try Databricks or Databricks Community Edition options. These options allow you to explore the platform’s features and capabilities without incurring costs. Once you are comfortable with Databricks, you can consider deploying it on your own cloud account for production workloads.
Let’s explore each option in detail:
Getting Started with Databricks Community Edition
Overview
Databricks Community Edition is a free, perpetual version of Databricks designed for learning and experimentation. It provides a simplified environment to explore Apache Spark and other Databricks features without incurring any costs.
Key Features and Benefits
No Cost: Access Databricks features without any financial commitment.
Educational Resources: Includes tutorials and resources to help users learn Apache Spark and Databricks functionalities.
Notebook Environment: Utilize notebooks for data exploration and visualization.
Single-Node Cluster: Offers a micro-cluster suitable for small-scale data processing tasks.
Limitations
Limited Resources: Restricted to a single-node cluster with limited memory and processing power.
Feature Restrictions: Does not support advanced features like Delta Lake, MLflow, Unity Catalog, or Databricks SQL.
Session Timeouts: Clusters automatically terminate after a period of inactivity, requiring manual restarts.
Storage Constraints: Limited to 10 GB of storage in the Databricks File System (DBFS).
No SLA or Support: Community Edition is provided “as-is” without official support or service-level agreements.
Create a Cluster: In the workspace, go to the “Clusters” section, click “Create Cluster,” provide a name, and select the default settings to launch your cluster.
Start Exploring: Begin creating notebooks and exploring Databricks features.
Post-Trial and Upgrading
Since the Community Edition is free and not time-limited, there is no trial period. However, if you require more resources or advanced features, consider upgrading to a paid Databricks plan, which offers enhanced capabilities and support.
Conclusion
Databricks Community Edition is an excellent starting point for individuals new to big data and Apache Spark. It provides a risk-free environment to learn and experiment, making it ideal for students, educators, and professionals exploring data analytics.
Getting Started with Express Setup
Overview
The Express Setup, also known as “Try Databricks,” offers a 14-day free trial of the Databricks platform. This option allows users to experience Databricks’ full capabilities without needing a cloud provider account. You only need an email address to get started.
Key Features and Benefits
Quick Access: Start using Databricks within minutes using only an email address.
Serverless Workspace: Automatically provisions a serverless workspace with pre-configured settings.
Free Credits: Receive usage credits valid for 14 days to explore various Databricks features.
Collaboration: Optionally enable automatic user provisioning for team collaboration.
Guided Experience: Access tutorials and documentation to assist with onboarding.
Trial Limitations:
While the free trial offers a valuable introduction to Databricks, it’s essential to be aware of certain limitations:
Compute Resource Limits: To optimize credit usage, Databricks imposes limits on the scaling of serverless compute resources. This includes:
Maximum one SQL warehouse per workspace, scaling up to 50 DBUs/hr.
Serverless compute for notebooks, jobs, and Delta Live Tables (DLT) scaling up to 50 DBUs/hr.
No access to GPUs (CPU-only).
Vector Search Limits: Vector search functionality is restricted to one endpoint with a scale capped at 1 vector search unit.
Network Access: External network access is limited during the trial. For full access, you’ll need to upgrade your account. If you need to access a blocked public dataset, Databricks recommends manually downloading the dataset and uploading it to your workspace.
Serverless Workspace Limitations: The trial’s serverless workspace has limitations related to serverless compute and default storage.
Limitations
Resource Caps: Limited to one SQL warehouse per workspace, scaling up to 50 DBUs/hr.
Compute Restrictions: Serverless compute for notebooks, jobs, and Delta Live Tables (DLT) also capped at 50 DBUs/hr.
No GPU Access: Only CPU-based processing is available during the trial.
Vector Search Limitations: Restricted to one endpoint with a maximum of one vector search unit.
Network Access: External network access is limited; certain public datasets may require manual upload.
Go to the Databricks website and click on
“Try Databricks” in the top right corner of the menu.
Select Express Setup: Click on “Get Started” and choose “Use express setup.”
Provide Email: Enter your email address and verify it through the code sent to your inbox.
Workspace Configuration: Name your workspace and select the desired region.
Access Workspace: Once set up, log in to your workspace to begin exploring Databricks features.
note
At the “Try Databricks” page, in the “Start your free trial” box, Click
on Already have an account? Log in
Post-Trial and Upgrading
Upgrade Options: Add a payment method during or after the trial to continue using Databricks without interruptions.
Data Retention: Assets created during the trial may be deleted 60 days after the trial ends if not upgraded.
Enhanced Features: Upgrading unlocks additional features, including custom compute configurations and increased resource limits.
Conclusion
The Express Setup is ideal for individuals and teams to quickly explore the Databricks Lakehouse Platform with no upfront commitment. It provides a serverless environment, free credits, and access to key features, making it ideal for hands-on learning, proof-of-concept projects and early-stage evaluations. However, it comes with important limitations—such as reduced compute resources and restricted network access—so users looking for production-ready capabilities should consider upgrading to a paid plan.
Getting Started with Databricks on Your Cloud
Overview
Deploying Databricks on your own cloud means integrating Databricks with your existing AWS, Azure, or Google Cloud environment. This option is designed for production workloads and offers full access to enterprise-grade features, scalability, and cloud-native security. While it requires cloud configuration and billing setup, it gives you the most flexibility and control.
Key Features and Benefits
Full Control: Manage your own compute resources, storage, and network configurations.
Scalability: Leverage the scalability of your cloud provider to handle large datasets and complex workflows.
Integration: Seamlessly integrate with existing cloud services and security protocols.
Customization: Tailor the Databricks environment to meet specific organizational requirements.
Support: Access to Databricks support and service-level agreements for enterprise deployments.
Limitations
Complex Setup: Requires familiarity with cloud infrastructure and permissions to provision resources.
Cost Management: Users are responsible for managing and monitoring cloud resource usage to control costs.
Initial Configuration: May involve more time and planning to set up compared to other options.
Setup Steps
Choose Cloud Provider: Decide whether to deploy on AWS, Azure, or Google Cloud.
Upgrading: After the 14-day free trial, you can upgrade by adding a billing account to your cloud workspace. You’ll retain all configurations and assets created during the trial.
Scale and Cost Control: As you upgrade, configure cost control strategies such as auto-scaling clusters, job scheduling, and monitoring.
Support Tiers: Gain access to premium support, SLAs, and enterprise collaboration features upon upgrading.
Conclusion
Deploying Databricks on your own cloud is the most robust and scalable option for organizations ready to move beyond experimentation. It provides a production-ready environment tailored to enterprise needs, enabling advanced analytics, machine learning, and data engineering workloads at scale.
