Lab_1_Aura_Setup

Lab 1: Neo4j Aura Setup and Exploration

In this lab, you will set up your Neo4j Aura database, restore the knowledge graph from a backup, and explore your graph visually.

Prerequisites

• Completed Lab 0 (environment setup)
• For Workshop SSO Login: Access to OneBlink credentials page (provided by your organizer)
• For Free Trial Signup: A valid email address

Part 1: Neo4j Aura Signup

There are two signup options for this lab. Please follow the signup process provided by your workshop organizer.

Option A: Workshop SSO Login (Recommended for organized workshops)

If your organizer has provided OneBlink credentials, use the SSO login process:

• Follow the Neo4j Aura SSO Login guide to log in using your organization's SSO credentials
• This option uses pre-configured workshop accounts

Option B: Free Trial Signup (For self-paced learning)

If you're completing this lab independently or your organizer has instructed you to create a free trial:

• Follow the Neo4j Aura Free Trial Signup guide to create your own account
• This option provides a 14-day free trial with an automatically created instance

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Create Your Database Instance

Note: If you signed up using the Free Trial option (Option B), your instance was already created during the signup process. You can skip ahead to Part 2: Restore the Backup.

1. After logging in, click on Instances in the left sidebar under "Data services", then click the Create instance button.

   

   If you already have existing instances, click the Create instance button in the top-right corner of the Instances page.

   

2. Configure your new instance with the following settings:

   • Select the Aura Professional plan
   • Set the Instance name to a unique name based on your name (e.g., ryans-lab-instance). If you have an error try another unique name by adding your initials or a number.
   • Set the Sizing to 4 GB RAM / 1 CPU
   • Enable Vector-optimized configuration under Additional settings

   

3. Click Create to provision your database instance.

4. Save your connection credentials immediately. When your instance is created, a dialog will appear showing your database credentials (Username and Password). Click Download and continue to save the credentials file.

   

CRITICAL: The password is only shown once and will not be available after you close this dialog. Download the credentials file and store it somewhere safe. You will need these credentials in later labs to connect your applications to Neo4j.

Part 2: Restore the Backup

After your Aura instance is running, restore the pre-built knowledge graph:

Step 1: Download the Backup File

1. Download the backup file from GitHub:
   • Download link: finance_data.backup
2. Save the file to a location you can easily find (e.g., your Downloads folder)

Step 2: Upload to Aura

1. Go to your instance in the Aura Console

2. Click the ... menu on your instance and select Backup & restore

   

3. Click Upload backup to open the upload dialog

4. Drag the finance_data.backup file you downloaded into the dialog:

   

5. Wait for the restore to complete - your instance will restart with the SEC 10-K filings knowledge graph

The backup contains:

• SEC 10-K filing documents from major companies (Apple, Microsoft, NVIDIA, etc.)
• Extracted entities: Companies, Risk Factors, Products, Executives, Financial Metrics
• Asset manager ownership data
• Text chunks with vector embeddings for semantic search

Part 3: Explore the Knowledge Graph

In this section, you will use Neo4j Explore to visually navigate and analyze your knowledge graph. You'll learn how to search for patterns, visualize relationships, and apply graph algorithms to gain insights from your data.

Step 1: Access the Aura Console

Go back to the Neo4j Aura console at console.neo4j.io.

Step 2: Open Explore

In the left sidebar, click on Explore under the Tools section. This opens Neo4j's visual graph exploration tool.

Click Connect to instance to connect to your database.

Step 3: Search for Asset Manager Relationships

In the search bar, build a pattern to explore the relationships between asset managers, companies, and risk factors. Type AssetManager, then select the OWNS relationship, followed by Company, then FACES_RISK, and finally RiskFactor.

This creates the pattern: AssetManager — OWNS → Company — FACES_RISK → RiskFactor

Step 4: Visualize the Knowledge Graph

After executing the search, you'll see a visual representation of the knowledge graph. The graph shows AssetManager nodes (orange) connected to Company nodes (pink) through OWNS relationships, and Company nodes connected to RiskFactor nodes (yellow) through FACES_RISK relationships. The visualization reveals how different asset managers are exposed to various risk factors through the companies they own.

Tips for Exploring:

Zoom and Pan

• Zoom: Scroll wheel or pinch gesture
• Pan: Click and drag the canvas
• Center: Double-click on empty space

Inspect Nodes and Relationships

• Click on a node to see its properties
• Click on a relationship to see its type
• Expand nodes to see more connections

Step 5: Access Graph Data Science

To analyze the graph structure, click on the Graph Data Science button in the left toolbar. This opens the data science panel where you can apply graph algorithms.

Step 6: Apply Degree Centrality Algorithm

Click Add algorithm and select Degree Centrality from the dropdown. This algorithm measures the number of incoming and outgoing relationships for each node, helping identify the most connected nodes in your graph.

Click Apply algorithm to run the analysis.

Step 7: Size Nodes Based on Scores

After the algorithm completes, you'll see a notification showing how many scores were added. Click Size nodes based on scores to visually represent the centrality - nodes with more connections will appear larger.

Step 8: Analyze the Results

The graph now displays nodes sized according to their degree centrality scores. Asset managers (pink/salmon nodes) that own more companies appear larger, making it easy to visually identify the most significant institutional investors in your dataset.

Next Steps

After completing this lab, continue to Lab 2 - Aura Agents to build an AI-powered agent using the Neo4j Aura Agent no-code platform.