Databricks Architecture Explained: Workspace, Clusters, Jobs, Workflows

Modern data teams need platforms that are flexible, fast, and easy to scale. As data volumes grow and use cases expand into analytics and AI, traditional tools often fail to keep up. Databricks has become a popular choice because it brings data engineering, analytics, and machine learning into one platform.

To use Databricks well, teams must understand databricks architecture and how its core components work together. Without this knowledge, projects often become slow, expensive, and hard to maintain.

In this blog, we explain Databricks architecture in a clear and practical way. We focus on four essential parts:

• Workspace

• Clusters

• Jobs

• Workflows

By the end, you will understand how Databricks is structured and how each component supports scalable data platforms.

Understanding Databricks Architecture at a High Level

Databricks is a cloud-native data platform built on Apache Spark. It runs on top of cloud providers like Azure, AWS, and Google Cloud. One of its biggest strengths is the clear separation between storage and compute.

This means:

• Data is stored in cloud storage like Azure Data Lake or S3

• Compute power is provided by clusters that can scale up or down

This design makes Databricks flexible and cost efficient.

At a high level, databricks architecture includes:

• A shared workspace for teams

• Compute clusters to process data

• Jobs that automate workloads

• Workflows that orchestrate tasks

• Governance and security controls

Each layer has a clear responsibility.

Databricks Workspace Explained

What Is a Databricks Workspace

A Databricks workspace is the environment where users interact with the platform. It is the place where teams write code, create notebooks, manage jobs, and monitor pipelines.

The workspace acts as the shared layer between people and the underlying compute.

Key Elements Inside a Workspace

Inside the workspace, users can access:

• Notebooks using Python, SQL, Scala, or R

• Databricks SQL dashboards

• Jobs and workflows

• Libraries and dependencies

• User permissions and roles

The workspace allows multiple users to collaborate at the same time.

Why the Workspace Matters

The workspace is central to collaboration. It ensures that:

• Engineers and analysts work in one system

• Code is shared and reused

• Experiments are visible to teams

• Changes are easier to review

A well-managed workspace improves speed and reduces rework.

Databricks Clusters Explained

What Is a Databricks Cluster

A Databricks cluster is a group of virtual machines that provide compute power. Clusters run Spark code and process data. They are created on demand and can be stopped when not in use.

This design gives teams full control over performance and cost.

Types of Databricks Clusters

Databricks supports different cluster types, each serving a specific purpose.

All-Purpose Clusters

These clusters are mostly used during development and exploration.

• Used for writing and testing notebooks

• Shared by multiple users

• Suitable for interactive workloads

All-purpose clusters are flexible but should not be used for long-running production jobs.

Job Clusters

Job clusters are created for a specific task and shut down once the task finishes.

• Ideal for scheduled pipelines

• Lower operational cost

• Better isolation between jobs

• Recommended for production workflows

Job clusters are a key best practice in databricks architecture.

Cluster Components

Each cluster contains:

• One driver node which plans and coordinates tasks

• Multiple worker nodes which execute tasks

• Spark runtime optimised for Databricks

• Autoscaling rules

Autoscaling helps clusters adjust resources based on demand.

Why Clusters Are Critical

Clusters directly affect:

• Job performance

• Data processing speed

• Stability of pipelines

• Overall platform cost

Poor cluster configuration is one of the most common causes of failure in Databricks projects.

Databricks Jobs Explained

What Is a Databricks Job

A Databricks job is an automated task that runs code without manual input. Jobs are used to operationalise notebooks and scripts.

A job can run:

• A notebook

• A Python file

• A SQL query

• A Spark task

Jobs are often scheduled to run hourly, daily, or based on triggers.

Common Use Cases for Jobs

Databricks jobs are commonly used for:

• Data ingestion

• ETL and ELT pipelines

• Data quality checks

• Machine learning training

• Reporting updates

They remove the need for manual execution.

Job Configuration Details

Each job includes:

• Cluster selection

• Task definition

• Schedule or trigger

• Retry logic

• Notifications and alerts

Good job configuration improves reliability and reduces failures.

Monitoring Jobs in Databricks

Databricks provides detailed job monitoring:

• Execution time

• Logs and errors

• Success and failure history

Teams can quickly identify issues and fix them.

Databricks Workflows Explained

What Is a Databricks Workflow

A Databricks workflow connects multiple jobs into a single pipeline. It defines how tasks depend on one another and in what order they should run.

Workflows are used to build end to end data pipelines.

Why Workflows Are Important

Real data platforms involve multiple steps. For example:

1. Ingest raw data

2. Clean and validate it

3. Apply business logic

4. Create analytics tables

5. Update dashboards

Workflows ensure this process runs smoothly and consistently.

Workflow Capabilities

Databricks workflows support:

• Multiple tasks

• Task dependencies

• Parallel execution

• Shared parameters

• Error handling

This makes pipelines scalable and easier to manage.

How Workspace, Clusters, Jobs, and Workflows Work Together

Understanding how components connect is central to databricks architecture.

A typical flow looks like this:

1. Engineers build notebooks in the workspace

2. Code is tested on an all-purpose cluster

3. Jobs are created from notebooks

4. Jobs run on job clusters

5. Workflows link jobs together

6. Pipelines run automatically

Each layer has a clear role, improving control and clarity.

Databricks Architecture with Medallion Pattern

Most Databricks platforms follow the Medallion architecture.

Bronze Layer

• Raw data ingestion

• Minimal transformation

• Full history stored

Silver Layer

• Cleaned and structured data

• Standard schemas

• Deduplicated records

Gold Layer

• Business-ready datasets

• Aggregated metrics

• Used for BI and AI

Jobs and workflows move data between these layers in a controlled way.

Security and Governance in Databricks Architecture

Databricks includes strong governance features to protect data.

Unity Catalog

Unity Catalog is the central governance layer. It controls:

• Table access

• Column permissions

• Data lineage

• Auditing

It supports enterprise compliance needs.

Access Control

Permissions can be applied at different levels:

• Workspace

• Cluster

• Schema

• Table

• Column

This ensures users only access what they need.

Common Databricks Architecture Mistakes

Teams often face problems because of design issues.

Common mistakes include:

• Using interactive clusters for production jobs

• Running full data refreshes

• Missing monitoring and alerts

• Mixing dev and production environments

• Weak access control

Avoiding these mistakes improves performance and trust.

Best Practices for Databricks Architecture

To build a strong platform:

• Separate development and production

• Use job clusters for pipelines

• Follow Medallion architecture

• Enable Unity Catalog early

• Monitor cost and performance

• Keep workflows modular

These practices reduce risk and improve scalability.

Conclusion: Building Databricks Architecture with Tenplus

A strong understanding of databricks architecture is essential for building reliable data platforms. Workspaces support collaboration. Clusters provide scalable compute. Jobs automate tasks. Workflows connect everything together.

Many organisations struggle because Databricks is powerful but poorly structured. Success depends on architecture, not tools alone.

This is where Tenplus adds value. Tenplus helps organisations:

• Design end to end Databricks architecture

• Set up secure workspaces and governance

• Implement efficient cluster strategies

• Build reliable jobs and workflows

• Apply Medallion architecture correctly

• Optimise cost and performance

• Deliver Proof of Concepts in 15 days

If your team wants to use Databricks with clarity and confidence, Tenplus provides the expertise and speed to do it right. Book a free PoC with Tenplus today!