The Azure Databricks workspace navigation panel provides an organized view of your Databricks environment, offering access to various functionalities and resources. Here’s an overview of the navigation panel components with examples:

1. Workspace

• Overview: The Workspace is the main interface where you manage notebooks, libraries, clusters, jobs, and other resources.
• Example:
  • Notebooks: These contain code, comments, and visualizations. You can create, edit, and run notebooks for data analysis or ML tasks.
  • Libraries: Access and manage libraries, including Python packages or JARs.
  • Clusters: Manage your compute resources for data processing.
  • Jobs: Schedule and monitor the execution of notebooks or jars.

2. Data

• Overview: This section allows you to access and manage data within your Databricks workspace.
• Example:
  • Tables: View and manage tables registered in your workspace.
  • Data Storage: Access external data sources like Azure Data Lake Storage, Blob Storage, or other cloud-based storage.
  • DBFS: Databricks File System for managing files and directories within Databricks.

3. Clusters

• Overview: Manages your compute resources, including creating, configuring, and monitoring clusters.
• Example:
  • Create Cluster: Define and set up new clusters based on your computational requirements.
  • Cluster List: View existing clusters, their configurations, and the status of each cluster.

4. Jobs

• Overview: Schedule and manage jobs for executing notebooks or JAR files.
• Example:
  • Create Job: Set up a job by defining the notebook, frequency, and other execution parameters.
  • Job List: Monitor the status of running and completed jobs.

5. Models

• Overview: Access machine learning models and workflows, including MLflow experiments.
• Example:
  • MLflow: Manage machine learning experiments, models, and deployment.

6. Collaboration

• Overview: Collaboration tools for sharing and collaborating on Databricks content.
• Example:
  • Users & Groups: Manage access control, assign permissions to users, or create user groups.
  • Workspace Access Control: Set permissions at workspace or folder levels.

7. Settings

• Overview: Configuration and settings for managing your Databricks workspace.
• Example:
  • Workspace Settings: Configure workspace-specific settings, such as clusters, libraries, or access controls.
  • Admin Console: Access workspace administration functionalities.

8. Search

• Overview: Allows you to search for specific content within the workspace.
• Example:
  • Notebook Search: Find notebooks by title, content, or associated tags.
  • Table Search: Search for specific tables registered in the workspace.

Business-oriented examples of how the Azure Databricks navigation panel is utilized

1. Workspace:

Business Perspective:

• Notebooks: Data analysts create notebooks for various business analyses, such as sales forecasting, customer segmentation, or marketing campaign analysis.
• Libraries: Data engineering teams manage libraries containing custom code, like machine learning algorithms or data processing functions, ensuring consistency across projects.
• Clusters: Scaling compute resources during peak times to handle large-scale data processing or reducing costs by terminating clusters when not in use.

2. Data:

Business Perspective:

• Tables: Data engineers and analysts access tables to query and analyze sales data, customer information, or product inventory.
• Data Storage: Importing external data sources like customer data from Azure Data Lake Storage, enabling comprehensive analysis across various datasets.
• DBFS: Storing and managing datasets or files essential for business intelligence reports or data transformations.

3. Clusters:

Business Perspective:

• Create Cluster: Configuring specific cluster types optimized for processing large volumes of data for real-time analytics or model training.
• Cluster List: Monitoring clusters to ensure optimal performance during critical business operations or adjusting configurations based on workload demands.

4. Jobs:

Business Perspective:

• Create Job: Scheduling jobs to automatically generate daily sales reports, run sentiment analysis on customer feedback, or update machine learning models.
• Job List: Monitoring the success of ETL processes, ensuring timely execution of critical business processes.

5. Models:

Business Perspective:

• MLflow: Data scientists managing and deploying machine learning models for recommendation engines, fraud detection, or personalized marketing campaigns.

6. Collaboration:

Business Perspective:

• Users & Groups: Assigning permissions and access levels to various teams or departments, ensuring secure access to sensitive business data.
• Workspace Access Control: Managing project-specific permissions to ensure the right stakeholders have access to relevant data and analyses.

7. Settings:

Business Perspective:

• Workspace Settings: Configuring security protocols, compliance standards, or resource allocation to align with business policies and data governance.
• Admin Console: Overseeing workspace-wide settings and monitoring usage metrics for cost optimization and compliance.

8. Search:

Business Perspective:

• Notebook Search: Quickly finding and accessing critical analysis reports or operational scripts.
• Table Search: Locating specific datasets or tables for financial reporting, business planning, or performance analysis.

How to create navigation panel for various tasks

Here’s a step-by-step explanation with examples of how you can use the Azure Databricks navigation panel for various tasks:

1. Workspace:

Example: Creating a Notebook

1. Navigate to Workspace > Notebooks.
2. Click on “Create” and select “Notebook”.
3. Choose the language (e.g., Python, Scala, SQL).
4. Give the notebook a name (e.g., “Sales_Analysis”).
5. Start coding or writing analysis in the notebook cells.

2. Data:

Example: Accessing Tables and Storage

1. Navigate to Data > Tables.
2. View existing tables like “sales_data” or “customer_info”.
3. Perform SQL queries or analysis on these tables.

Example: Accessing Data Storage

1. Navigate to Data > Data Storage.
2. Access external data sources like Azure Data Lake Storage or Blob Storage.
3. Mount external data for analysis using Databricks.

3. Clusters:

Example: Creating and Managing Clusters

1. Navigate to Clusters.
2. Click on “Create Cluster”.
3. Configure cluster specifications (e.g., instance type, auto-scaling).
4. Start the cluster and monitor its performance.

4. Jobs:

Example: Scheduling a Job

1. Navigate to Jobs.
2. Click on “Create Job”.
3. Select the notebook or JAR file to run as a job.
4. Define scheduling options (e.g., frequency, timeout).
5. Monitor the execution status of the job.

5. Models:

Example: Managing MLflow Experiments

1. Navigate to Models > MLflow.
2. Create and manage machine learning experiments.
3. Track model versions, parameters, and performance metrics.

6. Collaboration:

Example: Managing Users and Access

1. Navigate to Collaboration > Users & Groups.
2. Add users to groups or assign permissions to specific resources.
3. Control access levels for different teams or individuals.

7. Settings:

Example: Configuring Workspace Settings

1. Navigate to Settings > Workspace Settings.
2. Adjust configurations for clusters, libraries, or access controls.
3. Set up policies for security, compliance, or resource management.

8. Search:

Example: Searching for Content

1. Use the search bar in the navigation panel.
2. Search for specific notebooks, tables, or other resources by name, content, or tags.
3. Access the required content directly from the search results.