Semi-Structured Data 101

Prior to the advent of the cloud, most data was structured, tucked neatly away into databases or spreadsheets. Today, organizations have access to a much larger diversity of data in various formats. Semi-structured data, generated from sources such as IoT devices, mobile apps, and web pages, holds exceptional value if businesses can mine it effectively. This article examines exactly what semi-structured data is, the challenges associated with analyzing it, and the technologies that businesses are implementing to gain its full value.

What Is Semi-Structured Data?

Semi-structured data, or partially structured data, doesn’t follow the tabular structure associated with relational databases or other forms of data tables. However, it does contain tags and metadata to separate semantic elements and establish hierarchies of records and fields. 

How does semi-structured data differ from structured data?

Semi-structured and structured data are distinguished by two primary characteristics. First is schema. Unlike structured data, semi-structured data doesn’t require a prior schema definition like structured data does. Without a predefined,  fixed schema, semi-structured data is more flexible and free to evolve over time as new attributes are added. The second key differentiator is data structure. Semi-structured data supports a hierarchical data structure that contains nested information. In contrast, structured data simply represents data in a flat table. Semi-structured data’s nested data hierarchy makes it an ideal format for working with data received from apps and other internet-enabled devices. 

How does semi-structured data differ from unstructured data?

Unstructured data is raw data with no established data model or schema. Semi-structured data is unlike unstructured data in that it has some definite and consistent markers that create distinct semantic elements and impose an organizational hierarchy of records and fields within the data. 

Examples of Semi-Structured Data Formats

Semi-structured data comes in a variety of formats, based on the source they originate from. Here are a few of the most common:

XML: Extensible Markup Language (XML) has become one of the most popular semi-structured data formats. This versatile and easy-to-use markup language allows users to define tags and attributes required for storing data in a hierarchical form.

JSON: A commonly used alternative to XML, JavaScript Object Notation (JSON) collects semi-structured data from IoT devices, web browsers, and smartphones, then organizes that data into batches before transmitting it to a data platform via a data pipeline. This versatile format can also be used to transfer data in between servers and apps or internet-connected devices.

Avro: Originally developed for use with Apache Hadoop, Avro is a remote procedure call (RPC) framework and data serialization. Using schemas defined in JSON, Avro serializes data in a compact, binary format that can be sent to any app or program where it is deserialized. 

ORC: Optimized Row Columnar (ORC) is a semi-structured data format that was initially designed to achieve more-efficient compression and enhance performance for reading, writing, and processing data over earlier Hive formats.

Parquet: Another columnar storage file format similar to ORC, Parquet is designed for use in the Hadoop ecosystem. Parquet is ideal for working with complex data in large volumes and features different methods for efficient data compression and encoding types. 

Semi-Structured Data Sources

Semi-structured data is generated from a range of sources, including many popular consumer devices. This data format is becoming increasingly common and represents an enormous opportunity for businesses. The rise of powerful cloud platforms has made it possible to efficiently store, process,  and analyze semi-structured data, unlocking valuable insights that were previously out of reach. Here are a couple  common semi-structured data sources that provide a glimpse into the value of this type of data.

Internet of Things (IoT) sensors

IoT sensors produce data in multiple formats, including semi-structured data. These remote sensors have multitudes of uses and are capable of generating massive amounts of actionable data. For example, manufacturers use data from equipment-mounted sensors to monitor heat, vibration levels, and output to accurately predict when machinery will require maintenance. IoT sensors mounted on forklifts in warehouses can help optimize product picking routes, improving worker productivity and order fulfillment timelines. IoT devices also have many applications for healthcare settings, allowing physicians to monitor key metrics for high-risk patients by accessing data from wearable monitoring devices. This data can be collected and analyzed to assess patient adherence to treatment plans and track medically relevant information such as blood sugar levels over time.

Web data

The dramatic increase in semi-structured data is also attributable to the growth of the web. HTML, XML, and other markup languages are all considered semi-structured. Their schemas may be descriptive, partial, or evolving.  Semi-structured web data often contains lists and tables mixed with unstructured text. This data can be mined to show relational data in ways that unstructured data, such as plain text, cannot. Email is often the same, providing a mix of unstructured text mixed with structured data such as sender, recipient, time and date, etc. Given the sheer volume of online content and data produced daily, the ability to analyze these rich data sources requires modern data analytics systems.

Challenges with Analyzing Semi-Structured Data

Semi-structured data can be analyzed to uncover a wealth of actionable insights. But working with data in this format presents some challenges, especially for organizations working with legacy infrastructure.

Large data volumes

Semi-structured data is generated in very large quantities. IoT devices, sensors, and other data sources create near-constant streams of new data. Processing, storing, and analyzing data at scale requires data storage and compute power that exceed the resources available in most on-premises data warehouses. Running queries on billions of rows of data in real time requires the speed and power offered by a cloud data platform, which also has the benefit of being scalable so you only need to pay for the resources used at any given time. 

Semi-structured format

The semi-structured data format isn’t as easy to manage and analyze as structured data because semi-structured data is a text-based representation of structured data based on key-value pairs and ordered lists. This data format lacks a schema with files that can contain an arbitrary depth of nesting. For this reason, it’s necessary to have a cloud data solution that enables bringing all types of data into the chosen model with efficient pipelines. Additionally, the platform should provide native support for semi-structured data formats including JSON, Avro, ORC, Parquet, and XML to help conserve your IT team resources and provide faster time to insight.

Technical barriers

Parsing semi-structured data into an understandable schema is a time-consuming process, even for highly skilled data scientists. Complexities involved in this process have traditionally prevented organizations without access to large data teams from readily accessing the insights semi-structured data can produce. But new cloud technologies have overcome these barriers. Some data platforms enable data preparation for any amount of data or users with a multi-cluster compute architecture that supports autoscaling with few manual operations required.

Snowflake for Semi-Structured Data

The Snowflake Data Cloud empowers organizations to benefit from data from a variety of sources, including structured, unstructured, and semi-structured data. Snowflake is ideal for semi-structured data because it enables loading this data without prior transformation, detecting schema automatically while onboarding, transparently converting the data to an optimized internal storage format, and leveraging automatic query optimization. 

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. Using Snowpark, data teams can effortlessly transform raw data into modeled formats regardless of the type, including JSON, Parquet, and XML. 

Snowflake’s architecture makes it possible to join, window, compare, and calculate structured and semi-structured data in a single query. This capability eliminates extra systems and steps while ensuring superior performance, simplifying data pipelines and speeding preparation.

See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.