Semi-Structured Data

Semi-structured data are datasets that contain elements of structured and unstructured data

What is semi-structured data?

Semi-structured data refers to data not captured or formatted in conventional ways. It doesn’t follow the tabular structure associated with relational databases or other forms of data tables because it doesn’t have a fixed schema. However, the data is not completely raw or unstructured and does contain some structural elements such as tags and metadata. These elements establish hierarchies of records and fields, making it easier to analyze.


While semi-structured data can be more challenging to work with than structured data, it offers greater flexibility and adaptability, making it a valuable tool for data analysis and management.

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What is the difference between structured, unstructured, and semi-structured data?

The following comparisons explain what makes semi-structured data different from unstructured and structured data.


Semi-structured data vs. unstructured data

Unstructured data is information that doesn’t have a predefined format or schema, so it can’t be stored in a traditional relational database. Semi-structured data is unlike unstructured data in that it has some structural elements, such as tags and metadata, that impose an organizational hierarchy of records and fields within the data.


Semi-structured data vs. structured data

Semi-structured and structured data are distinguished by two primary characteristics: schema and data structure.


Unlike structured data, semi-structured data doesn’t require a prior schema definition, which makes it more flexible for data evolution. Also, semi-structured data supports a structure that contains a nested data hierarchy, whereas structured data is in a flat table. The nested structure makes semi-structured data an ideal format for working with data received from IoT devices.

Characteristics of semi-structured data

  • It doesn’t conform to a data model but has some structure
  • It doesn’t need a fixed schema before storage, which allows for greater flexibility in terms of the structure and kinds of data that can be stored
  • It contains metadata used to group data and organize it in a hierarchy
  • It can’t be stored in the form of rows and columns in a relational database

Semi-structured data examples

Semi-structured data is becoming increasingly common as organizations collect and process more data from various sources like social media and IoT devices. Examples of semi-structured data include:

XML documents: This is one of the most popular semi-structured data formats. XML is a versatile and easy-to-use markup language that allows users to define tags and attributes required for storing data hierarchically.


JSON: JSON is used to collect semi-structured data from IoT devices, web browsers, and smartphones, and then organize it into batches and transfer it to a data platform.


HTML code, graphs and tables, and emails are other examples of semi-structured data often found in object-oriented databases.

Benefits and challenges of semi-structured data

Flexibility is the greatest strength of semi-structured data, but it also introduces some issues you won’t find with structured data. Here are the most significant benefits and challenges:



  • Flexible and simpler to scale compared to structured data
  • Adaptable to evolving data sources
  • Self-describing nature ensures that the context and meaning of data are embedded within the data, aiding in understanding and interpretation
  • Semi-structured data balances easy human inspection and efficient computational processing, making it suitable for a wide range of applications, from web services to data analytics



  • The lack of a fixed schema can lead to scalability issues
  • Querying and extracting insights can be challenging and time-consuming, often requiring specialized tools and expertise to process the data effectively
  • Flexibility can lead to inconsistencies in data representation, making aggregation and analysis difficult due to variations in structure or missing elements

Techniques for analyzing semi-structured data

You can use the following techniques to analyze semi-structured data:

  • Graph-based modeling
  • Extensible markup language (XML)
  • Exploratory data analysis
  • Pattern recognition
  • Text analytics
  • Sentiment analysis
  • Anomaly detection

Semi-structured data tools

You can store, process, and analyze semi-structured data using various tools. For example:

  • NoSQL databases like Couchbase and MongoDB™ are designed to handle semi-structured data
  • You can use XML and graph-based modeling to define attributes, exchange information, and index data in a hierarchical order


Non-relational databases, or NoSQL databases, are becoming increasingly popular due to their ability to handle semi-structured or unstructured data. They use a variety of data models to accommodate diverse data types and structures, making them well suited for handling large, complex datasets that may evolve.

Couchbase is a distributed database that supports both key-value and document data models. It’s designed for high scalability, performance, and availability and supports features such as auto-sharding, in-memory caching, and full-text search. Couchbase is well suited for handling large datasets and high write throughput, making it popular for e-commerce, gaming, and social media applications.

Visit our Concepts Hub to learn more about structured, unstructured, and semi-structured data and many other database-related topics.

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