Kiara Documentation
  • Before you begin
    • What is kiara?
    • What can kiara do?
    • What is data orchestration?
    • Key concepts
  • Installation
    • Mac
    • Windows
  • Using kiara
    • Network analysis in jupyter notebook
    • Topic modeling in jupyter notebook
    • in CLI
    • Use case
  • Customisation
    • Creating modules
    • Creating pipelines
  • Troubleshooting
    • Versioning
    • Reporting problems
    • Tips for beginners
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  • From Sources to Data
  • Data Orchestration, documented
  1. Before you begin

What is data orchestration?

Data orchestration is the process of managing and conducting data-related tasks. In kiara, you can organize, transform, and track your research data throughout the analytical process.

From Sources to Data

Historical sources don't arrive as neatly structured data. Letters, census records, newspapers, and other primary sources must be transformed into machine-readable formats before computational analysis becomes possible. This transformation process, in which you create your dataset, involves critical decisions that shape your research outcomes. Yet many digital tools obscure that process. When you simply click through software interfaces, you may lose sight of how your sources become data, how that data is manipulated, and ultimately how your interpretations relate to the original materials.

Data Orchestration, documented

Kiara therefore allows you to both perform and track each of those steps. Rather than processing your data through monolithic "black box" tools, kiara facilitates the creation of data pipelines that consist of distinct, interconnected modules. Each module has clearly defined inputs, outputs, and processes, allowing you to understand exactly what is happening to your data at each step. At the same time, kiara automatically tracks the ancestry of your data through every transformation. At any point, you can see how a particular dataset was created, including all the inputs and processes involved, making your workflow and findings both explainable and reproducible.

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Last updated 1 month ago