Structured Data Versus Semi-Structured Data

In the past, business intelligence dealt with structured data only. Just two decades ago, business intelligence data was primarily sourced from spreadsheets and databases. But in today’s world, organizations are generating and accessing a much greater variety of data. Apps, mobile devices, and the Internet of Things (IoT) are all creating valuable data that can be used to inform business decisions, harness opportunities, and solve challenges.

Unlike data found in spreadsheets and databases, this data doesn’t come in a fully structured form—it’s semi-structured. This article explains the difference between structured and semi-structured data and why your data platform must support both for your organization to be data-driven. It also explores what to consider as you evaluate a data platform to empower your organization’s business intelligence initiatives.

What Is Structured Data?

Structured data is rigidly formatted and typically contained in rows and columns. It can be easily queried in a relational database. Structured data can be generated by a human, such as in a spreadsheet, or by a machine, such as through a point-of-sale (POS) system. Structured data is estimated to make up less than 20% of all business intelligence data today.

What Is Semi-Structured Data?

Semi-structured data is not entirely devoid of structure, but the structure is not as rigid as that of a relational database. Organizational properties such as semantic tags and metadata make it easier to search using hierarchies and categories. Common semi-structured data formats include JSON, Avro, and XML. You’re looking at semi-structured data when you see a smartphone photo or video, which contain unstructured data such as the image and audio itself, as well as structured data such as a time stamp and geotag.

2 Differences Between Structured and Semi-Structured Data

Two key differences distinguish structured and semi-structured data. The significance of these differences explains why semi-structured data is so ubiquitous in the modern world and why data-driven organizations must use tools designed for both semi-structured data and structured data for business intelligence.

1. Schema

Whereas structured data requires a fixed schema defined in advance, semi-structured data does not require a prior schema definition. For this reason, it can constantly evolve—new attributes can be added at any time. 

2. Nested data structure

Structured data represents data in a flat table. In contrast, semi-structured data can contain hierarchies of nested information. This nested data structure makes semi-structured data ideal for the variety of data coming from apps, devices, and channels.

The flexibility of schemaless design and the ability to represent a wide range of information are important reasons that semi-structured data has become so common.

Why Your Data Platform Must Support Both Data Types

Conventional data warehouses were designed when data arrived in very predictable structured formats. Data sources were limited, controlled, and changed infrequently since relational data with fixed schemas was the norm. 

Today, however, data is being generated in diverse forms from diverse sources, primarily driven by the rapid decrease in the cost of storing data and the growth in distributed systems. This explosion of machine-generated data poses challenges, but it enables organizations to be significantly more data-driven.

Because most data coming from apps, APIs, mobile devices, and IoT devices is in semi-structured data forms such as JSON and Avro, your data platform should support semi-structured data in addition to structured data. If you’re working with a conventional data warehouse, which doesn’t support semi-structured data, you’ll need to format your data before making use of it.

What to Consider When Evaluating a Cloud Data Platform

To take advantage of all the data available to your organization, your data platform must support the variety of data being generated. But it should also allow you to easily scale your business intelligence activities and optimize your maintenance resources. Here are two considerations to help you evaluate a data platform.

1. The amount of data you plan to store and associated costs

A cloud data platform leverages the cloud’s scalability. If you plan to store a significant volume of data from various sources, you need a platform that allows you to support thousands of users and hundreds of billions of rows of data. Additionally, the cloud is highly cost-effective, so a cloud data platform should enable affordable storage of these volumes of data.

2. Your level of engineering resources

Your cloud data platform should enable your organization to efficiently make use of all the data you have, even if your data team isn’t a large one. To accomplish this, it should offer near-zero maintenance and easily integrate with the best data pipeline, storage, and analytics tools to automate as much of the process as possible. A robust data platform will level the playing field, giving your organization the ability to conduct business intelligence like larger, well-resourced competitors.

Snowflake for All Forms of Data

Snowflake’s Data Cloud empowers organizations to unite data from a variety of sources, easily discover and securely share governed data, and execute diverse workloads—from data warehousing to data science. It’s designed for organizations of all sizes to efficiently make use of vast volumes of data from all varieties of sources. Snowflake is ideal for semi-structured data, and it handles this data as a first-class database element: 

• Flexible-schema data type: Load semi-structured data without transformation.
• Storage optimization: Transparently convert data to an optimized internal storage format.
• Query optimization: Leverage automatic database optimizations for fast and efficient SQL querying.

Snowflake’s architecture makes it possible to query semi-structured data and structured data together using SQL. You can join, window, compare, and calculate structured and semi-structured data in a single query. This capability makes it possible to eliminate extra systems and steps while experiencing superior performance, which simplifies data pipelines and reduces the time from when data is generated to when it can be accessed and analyzed.

Additionally, Snowpark is a developer framework for Snowflake that allows data engineers, data scientists, and data developers to execute pipelines feeding ML models and applications faster and more securely in a single platform using SQL, Python, Java, and Scala. Leveraging SQL and Python, Scala, or Java in Snowpark allows data teams to effortlessly transform raw data into modeled formats regardless of the type, including JSON, Parquet, and XML. 

To test drive Snowflake’s capabilities, sign up for a free trial.