Databricks Workspace Resources – Complete Guide

Databricks Workspace Resources – Full Guide

A Databricks workspace provides an environment where you can create, organize, and manage compute resources, data objects, automation workflows, analytics assets, and machine learning components. The Databricks UI supports creating notebooks, queries, dashboards, jobs, pipelines, experiments, models, and more through the + New menu and the workspace sidebar [1](https://learn.microsoft.com/en-us/azure/databricks/jobs/pipeline). Workspace objects include notebooks, jobs, libraries, data files, experiments, and more [2](https://www.youtube.com/watch?v=cNFKzWpRvsw).

Summary Table of Creatable Databricks Workspace Resources

Resource	Description	How to Create (UI Steps)
Notebook	Interactive document for Python, SQL, R, Scala code execution [2](https://www.youtube.com/watch?v=cNFKzWpRvsw).	1. Click + New → Notebook. 2. Enter notebook name. 3. Choose language. 4. Select compute (cluster not covered). 5. Click Create [1](https://learn.microsoft.com/en-us/azure/databricks/jobs/pipeline).
Query	SQL query used for dashboards & alerts [1](https://learn.microsoft.com/en-us/azure/databricks/jobs/pipeline).	1. Click + New → Query. 2. SQL Editor opens. 3. Select SQL Warehouse. 4. Write SQL and click Save.
Dashboard	Visual BI dashboard created from queries [1](https://learn.microsoft.com/en-us/azure/databricks/jobs/pipeline).	1. Open a saved query. 2. Click Add to Dashboard. 3. Create new or choose existing. 4. Arrange visuals → Save.
Alert	Condition-based SQL alert [1](https://learn.microsoft.com/en-us/azure/databricks/jobs/pipeline).	1. Open a SQL Query. 2. Click Create Alert. 3. Add condition + recipients. 4. Save.
Repo	Git-connected source repo [1](https://learn.microsoft.com/en-us/azure/databricks/jobs/pipeline).	1. Click + New → Repo. 2. Choose Git provider. 3. Paste repository URL. 4. Authenticate and click Create.
File	Workspace-level file (CSV, Python script, config) [2](https://www.youtube.com/watch?v=cNFKzWpRvsw).	1. Open Workspace browser. 2. Click Add → File Upload. 3. Upload file.
Library	Install Python/JAR packages for use in notebooks/jobs [2](https://www.youtube.com/watch?v=cNFKzWpRvsw).	1. Go to Workspace → Libraries. 2. Click Install New. 3. Upload wheel/JAR or specify PyPI package. 4. Click Install.
Job	Automation for notebooks, scripts, JARs, pipelines [2](https://www.youtube.com/watch?v=cNFKzWpRvsw).	1. Click Jobs in sidebar. 2. Click Create Job. 3. Name the job. 4. Click Add Task and choose task type. 5. Configure task details. 6. Assign compute (cluster selection only). 7. Add schedule if needed. 8. Click Create [1](https://learn.microsoft.com/en-us/azure/databricks/jobs/pipeline).
Pipeline	DLT / Lakeflow ETL pipelines (triggered or continuous) [3](https://docs.databricks.com/aws/en/getting-started/concepts).	1. Click Jobs & Pipelines. 2. Click Create Pipeline. 3. Enter name. 4. Select pipeline mode. 5. Add SQL/Python pipeline code. 6. Select target catalog/schema. 7. Configure settings. 8. Click Create.
Experiment	MLflow tracking experiment for ML models [1](https://learn.microsoft.com/en-us/azure/databricks/jobs/pipeline).	1. Click + New → Experiment. 2. Enter name and location. 3. Click Create.
Model	MLflow model stored in Model Registry [4](https://devstacktips.com/development/programming-languages/2025/06/06/mastering-databricks-jobs-api-build-and-orchestrate-complex-data-pipelines/).	1. Open an MLflow run. 2. Click Register Model. 3. Select or create model name. 4. Register.
Serving Endpoint	Real-time inference endpoint for ML models [1](https://learn.microsoft.com/en-us/azure/databricks/jobs/pipeline).	1. Click + New → Serving Endpoint. 2. Select model. 3. Configure autoscaling. 4. Click Create Endpoint.

Visual Diagram of All Databricks Workspace Resources

The following diagram shows how notebooks, pipelines, jobs, dashboards, alerts, and ML workflows connect logically inside a Databricks workspace.

```mermaid
flowchart TD

A[Notebook] --> B[Job]
B --> C[Pipeline]
C --> D[Tables / Data Assets]

A --> E[Experiment]
E --> F[Model]
F --> G[Serving Endpoint]

B --> H[Dashboards]
H --> I[Alerts]

J[Repo] --> A
K[Files / Libraries] --> A