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User Guide

Welcome to the Sigilweaver User Guide. This documentation covers everything you need to build data transformation workflows.

Getting Started​

Sigilweaver is a visual data pipeline tool that lets you build complex data transformations through an intuitive drag-and-drop interface. Under the hood, it uses Polars for fast, efficient data processing with lazy evaluation.

Coming from another tool?

If you're switching from Tableau Prep, Alteryx, Power Query, KNIME, Orange, or Excel, check out our Crash Courses to quickly map what you already know to Sigilweaver's approach.

Documentation Sections​

Interface​

Learn how to navigate the application:

Expressions​

Master the expression language used in Filter and Formula tools:

  • Basics - Column references, literals, and operators
  • Syntax Rules - Parentheses, multi-line expressions, and gotchas
  • Common Operations - String manipulation, dates, casting, and null handling

Data Types​

Understand the data types Sigilweaver supports and when to use each.

Tools​

Reference documentation for every tool:

CategoryTools
Input/OutputInput, Output
PreparationFilter, Select, Sort, Formula
JoinUnion, Join
AggregateSummarize

Core Concepts​

Lazy Evaluation​

Sigilweaver uses Polars' lazy evaluation model. This means:

  • Operations are not executed immediately when you build the workflow
  • The entire pipeline is optimized before execution
  • Data is processed efficiently in a streaming fashion when possible

This allows you to work with datasets much larger than your available memory.

Workflows​

A workflow is a directed acyclic graph (DAG) of tools connected by wires:

  • Tools perform operations on data (filtering, joining, aggregating, etc.)
  • Wires connect tool outputs to tool inputs, defining data flow
  • Sockets are the connection points on tools (inputs on the left, outputs on the right)

Expressions​

Many tools use Polars expressions for configuration. Expressions let you:

  • Reference columns: pl.col("column_name")
  • Apply transformations: pl.col("price") * 1.1
  • Filter rows: pl.col("status") == "active"
  • Combine conditions: (pl.col("a") > 10) & (pl.col("b") < 20)

See the Expressions section for a complete guide.

External Resources​