Chaining Nodes Using Edges

Chaining nodes is especially useful whenever your data represents an ordered sequence. Even a simple column of values can become a meaningful visual path once edges are created automatically. Common scenarios include:

• Timelines: Turning a list of dates or milestones into a left‑to‑right sequence that shows how events unfold.
• Workflows and Processes: Visualizing approval steps, onboarding stages, or any linear process where one step leads to the next.
• Pipelines: Representing ETL stages, data transformations, or processing phases in the order they occur.
• Queues or Ordered Lists: Showing the exact order in which tasks, tickets, or operations are handled.
• Version Progression: Displaying how versions evolve over time, such as v1.0 → v1.1 → v1.2 → v2.0.
• Routes or Paths: Mapping a sequence of locations, checkpoints, or waypoints into a clear path diagram.
• Dependency Chains: Illustrating simple “A must happen before B” relationships without needing a full dependency tree.
• Learning or Reading Paths: Presenting a recommended sequence of topics, lessons, or readings.

These scenarios all benefit from the automatic edge generation provided by CREATE EDGES, allowing you to transform a basic list into a structured, easy‑to‑read graph with minimal SQL.

Assume you have an Excel workbook with a worksheet named Alphabet that contains a column called letter with four rows of data: A, B, C, and D. Your goal is to chain these values together in sequence.

The following SQL creates nodes A, B, C, D:

SELECT [letter] AS [Item] from [Alphabet$]

The CREATE EDGES SQL extension automatically generates edges like A -> B, B -> C, and C -> D.

The SQL is specifed as follows:

SELECT DISTINCT [letter] AS [Item], 
       TRUE              AS [CREATE EDGES] 
FROM [Alphabet$]
ORDER BY [letter] ASC

The DOT source code appears as:

strict digraph "Relationship Visualizer"
{
    rankdir=LR;
    A -> B;
    B -> C;
    C -> D;
}

Producing this graph: