It makes sense that data professionals can get quite accustomed to operating in a binary world. Data flows through an ETL pipeline, or it doesn’t. Data will either meet the standards of an organization’s data quality rules, or it won’t. Downstream data consumers will either bring you candy and treats due to the quality they enjoy, or (unfortunately, we hate to say it) they won’t.
To be, or not to be…validated by an automated hand, as data doth embark upon its journey from the very dawn of its lifecycle (as it were).
But this binary “bias” can cause some confusion when professionals run into aspects of the data engineering world that are neither this nor that—semi-structured data being a prime example.
That said, the reasons can be less than obvious depending on one’s background and experience.
So let’s make sure we know what semi-structured data is, how being both structured and unstructured can create some semi-serious issues, and how specific solutions (like data contracts) can help min/max this category of data in practice.
What is semi-structured data?
Semi-structured data is a type of data that doesn’t conform to the rigid structure of traditional databases but still contains some organizational properties. It exists between structured data, which fits neatly into tables with predefined schemas, and unstructured data, which lacks a specific format or organization.
In the world of data analytics—and, by extension, big data—semi-structured data plays a critical role by offering a flexible model for storing and managing data that doesn’t fit into neat categories the way so many other aspects of data engineering do.
This flexibility allows companies to capture diverse data formats, such as JSON, which is commonly (and increasingly) used for data transfer in APIs and IoT devices. JSON’s flexibility allows developers to easily transmit data without needing to adhere to a rigid schema.
Examples of semi-structured data
On the whole, semi-structured data stands apart because it doesn’t adhere to a rigid, tabular schema like structured data, but it also isn’t as freeform as unstructured data. Here’s why these examples fall into the semi-structured category:
- Lack of fixed schema: Unlike structured data, semi-structured data doesn’t require a fixed schema. This flexibility allows it to handle various data formats, which makes it ideal for applications that process big data from multiple sources.
- Use of tags and metadata: Semi-structured data uses tags or metadata to provide a level of organization. This allows for complex relationships between data elements, which is crucial for managing and analyzing data that comes from diverse sources like web logs, IoT sensors, or mobile devices.
- Hierarchical organization: Formats like XML and JSON allow for hierarchical relationships between data elements, making them ideal for storing and querying nested structures in large-scale data management systems or data lakes.
The hybrid nature of semi-structured data makes it a practical choice for companies needing flexible data models that can adapt to changing requirements while still enabling efficient querying and data analysis.
Semi-structured data balances structure and flexibility. Although it doesn’t follow the strict rules of relational databases, it still uses metadata, tags, or markers to create an organizational framework that unstructured data lacks. As a result, semi-structured data can take a multitude of forms:
- XML (Extensible Markup Language): XML allows users to define custom tags and attributes, providing a hierarchical structure. This is especially useful for exchanging complex data structures between systems, including data warehouses or other large-scale data management systems.
- JSON (JavaScript Object Notation): JSON organizes data using key-value pairs and supports nested structures. Widely used in APIs—where data is transmitted between clients and servers—JSON is easy to use and store.
- HTML (HyperText Markup Language): HTML, used to create web pages, is considered semi-structured because it organizes content hierarchically with tags. This structure allows for data analysis of web-based content, enabling search engines and content management systems to efficiently organize and retrieve information.
- Emails: Emails combine structured elements (like sender, recipient, date, and subject) with unstructured content (the body of the email). This makes emails a mix of structured and unstructured data, often requiring specialized tools for data analytics to extract useful insights.
- NoSQL database records: NoSQL databases are commonly used to store semi-structured data. They do not require a fixed schema, allowing each record to have a different structure. This makes NoSQL databases flexible but also presents challenges in storing data efficiently as datasets scale.
The different ways companies use semi-structured, structured, and unstructured data
This versatility is a boon for organizations, as semi-structured data is used to integrate disparate systems, manage evolving data requirements, and unlock insights from diverse sources. Here's how semi-structured data compares to structured and unstructured data in real-world use cases:
Uses for semi-structured data
- Flexibility and scalability: Semi-structured data formats like JSON and XML allow companies to store and process information without a rigid schema. This flexibility is particularly useful in data warehouses and data lakes, where data from various sources needs to be integrated without heavy restructuring.
- Data integration: Semi-structured data helps companies integrate information from multiple platforms, enabling smoother data exchange and consolidation. This is especially important in industries dealing with large volumes of big data, such as ecommerce, where information flows from different systems (web, mobile, APIs).
- Use cases: Semi-structured data is commonly used in data analytics applications like sentiment analysis, natural language processing (NLP), and log analysis. Its ability to handle irregular and incomplete datasets allows companies to extract meaningful insights, even from fragmented or complex data sources.
Uses for structured data
- Ease of use and analysis: Structured data is highly organized and stored in relational databases with predefined schemas, making it easier to query and analyze using standard tools like SQL. This type of data is often used in systems where predictability and consistency are key, such as financial databases or data warehouses.
- Predictability and reliability: The rigid schema of structured data ensures efficient storage and retrieval, making it ideal for applications that require consistent, reliable outputs, such as CRM systems or inventory management.
Uses for unstructured data
- Rich insights and innovation: Unstructured data, such as text, images, videos, and social media content, provides deep insights that structured data alone cannot offer. Companies use this type of data for data analytics projects that involve sentiment analysis, market research, and customer feedback, gaining insights that can drive innovation.
- Complexity and tools: Analyzing unstructured data often requires advanced tools like machine learning or data analytics platforms. Although complex, these tools allow businesses to tap into valuable qualitative insights hidden within vast amounts of unstructured data.
By leveraging the unique strengths of each data type—semi-structured, structured, and unstructured—companies can tailor their data management strategies to meet different analytical needs and business objectives.
Solutions for managing semi-structured data
As mentioned, the “this and that”-ness of semi-structured data leads to some particular challenges. But these challenges, fortunately, can be minimized (if not mitigated) through enacting simple yet impactful solutions:
- Leveraging NoSQL Databases: Certain databases—such as MongoDB and Couchbase—are well-suited for storing semi-structured data due to their flexible schemas, which can handle varied data formats efficiently.
- Adopt a Schema-on-Read Approach: This method applies a schema only when the data is queried, rather than during ingestion. Tools like Apache Hive, Hadoop, and AWS Athena use this approach, making it easier to process large amounts of data in a data warehouse or data lake environment.
- Make data validation consistent: Regular data validation and transformation processes can help ensure data quality by checking for consistency, completeness, and accuracy across semi-structured datasets.
- Implement data contracts: Data contracts ensure that the structure and quality of semi-structured data are maintained as it flows through different stages of the pipeline. These contracts act as agreements between data producers and consumers, setting clear rules for what the data should look like—and ensuring that edge cases or inconsistencies (such as missing fields in nested JSON objects) are caught early.
By employing these strategies (the more, frankly, the better), companies can efficiently manage semi-structured data alongside structured and unstructured data, ensuring their data management efforts scale with business needs.
Minimizing the slings and arrows of semi-structured data
In the world of data, much like Hamlet’s existential question, to be or not to be validated becomes a crucial decision point for data pipelines. Whether data conforms to the contracts set by automated governance tools or needs to be rejected and refined marks the difference between data that is ready to deliver insights and data that just adds noise to the system.
The ambiguity of semi-structured data may leave you wondering how to bring order to this complexity. Should it be treated as structured, unstructured, or somewhere in between? The answer lies in using the right tools to ensure it’s properly validated, integrated, and governed from the moment it enters your system.
At Gable, we’ve crafted a solution to make this choice easier. Sign up for our waitlist to learn how we help data teams resolve the “to be or not to be” dilemma with automation, contracts, and governance solutions that are purpose-built for the challenges of semi-structured data.
Don’t let your data’s destiny hang in the balance—make haste to Gable.ai to claim your spot on our data contract product waitlist!
