Published on 2025-06-28T08:29:58Z

What is Semi-Structured Data? Examples for Semi-Structured Data

Semi-structured data is a hybrid form of data that does not conform to a rigid relational schema but still contains tags or markers to separate semantic elements. It strikes a balance between structured data (e.g., SQL tables) and unstructured data (e.g., plain text), offering both flexibility and some organizational consistency. In analytics, semi-structured data often appears as JSON event payloads, XML logs, or tagged CSV files capturing user interactions, metadata, and nested attributes. This format enables modern analytics platforms to ingest diverse data sources without upfront schema definitions, supporting schema-on-read approaches and real-time event tracking. Tools like Plainsignal and Google Analytics 4 (GA4) are built to process and analyze semi-structured data at scale, parsing nested fields and dynamically adapting to evolving event schemas. Understanding semi-structured data is crucial for building flexible pipelines, ensuring data quality, and extracting meaningful insights from complex, evolving data streams.

Illustration of Semi-structured data
Illustration of Semi-structured data

Semi-structured data

Data that combines organizational tags with flexible schema, often seen in JSON or XML event payloads.

Definition & Key Characteristics

Semi-structured data contains organizational markers (like tags or keys) but does not require a fixed schema. It supports nested structures, optional fields, and varying record shapes, making it adaptable to changing data requirements.

  • Self-describing structure

    Each record carries its own metadata (e.g., keys in JSON) that describe the contained values, enabling parsers to interpret fields without an external schema.

  • Flexible schema

    Fields can be added, removed, or modified over time without breaking existing pipelines or requiring schema migrations.

  • Hierarchical organization

    Supports nested objects or arrays, allowing complex relationships and multi-level data to be captured in a single record.

Common Formats & Examples

Analytic teams encounter semi-structured data in multiple formats. Each format uses tags or delimiters to convey structure without enforcing a global schema.

  • Json (javascript object notation)

    Lightweight, text-based format ideal for event payloads, API responses, and configuration files. Widely used in web analytics for tracking user actions.

  • Xml (extensible markup language)

    Tag-based format with customizable element names. Common in legacy systems and integrations where verbose metadata is required.

  • Tagged csv / tsv

    Delimited text files where header rows or in-line markers define field names, though nesting is limited compared to JSON or XML.

Relevance in Analytics

Modern analytics relies heavily on event-driven architectures and user interaction data, which naturally produce semi-structured records.

  • Event tracking

    User clicks, page views, or custom events are sent as JSON payloads containing properties like event name, timestamp, and user metadata.

  • Log ingestion

    Server and application logs often emit JSON or XML entries with varying fields for errors, performance metrics, and contextual data.

How SaaS Tools Handle Semi-Structured Data

Leading analytics platforms provide parsers and schema-on-read engines to ingest and query semi-structured data without requiring upfront schema definitions.

  • Plainsignal (cookie-free simple analytics)

    PlainSignal captures page views and events via a minimal JavaScript snippet that emits JSON-style records. Example integration:

    <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
<script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>

  • Google analytics 4 (ga4)

    GA4 uses an event-based model where each interaction is a JSON-like record. It automatically parses nested parameters (e.g., user_properties) and supports dynamic event schemas via the Measurement Protocol.

Best Practices for Working with Semi-Structured Data

To maintain data quality and query performance, apply disciplined practices when ingesting and processing semi-structured records.

  • Adopt schema-on-read

    Delay schema enforcement until query time, enabling ingestion of evolving datasets without upfront transformations. Use tools like Apache Drill, BigQuery, or Snowflake VARIANT columns.

  • Validate & cleanse payloads

    Implement lightweight validation (e.g., JSON schema checks) to ensure required fields are present and types match expectations before analytics processing.

  • Index key attributes

    Extract frequently queried fields into indexed or parquet columns to improve query speed and reduce compute costs in large datasets.

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