What is structured data?
Structured data is data that has a standardized format for efficient access by software and humans alike. It is typically tabular with rows and columns that clearly define data attributes. Computers can effectively process structured data for insights due to its quantitative nature. For example, a structured customer data table containing columns—name, address, and phone number—can provide insights like the total number of customers and the locality with the maximum number of customers. In contrast, unstructured data, like a list of social media posts, is more challenging to analyze.
What are the features of structured data?
Here are some features and examples of structured data.
Structured data has the same attributes for all data values. For example, every booking record could have these attributes: booking name, event name, event date, and booking amount.
Structured data tables have common values that link different datasets together. For example, you can relate customer data with booking data by using customer id and booking id fields. So, you can store structured data conveniently in a relational database.
Structured data lends well to mathematical analysis. For example, you can count and measure the frequency of attributes and perform mathematical operations on numerical data.
You can store structured data in relational databases and manage it using structured query language (SQL). SQL lets you define a data model called a schema under which you determine preset rules—such as fields, formats, and values—for your data. You can then store structured data in data warehouses or other relational database technology.
Structured data examples
Here are examples of structured data systems:
- Excel files
- SQL databases
- Point-of-sale data
- Web form results
- Search engine optimization (SEO) tags
- Product directories
- Inventory control
- Reservation systems
What are the benefits of structured data?
There are several benefits of using structured data.
Ease of use
Anyone can quickly comprehend and access structured data. Operations such as updating and amending structured data are straightforward. Storage is efficient, as fixed-length storage units can be allocated for data values.
Structured data scales algorithmically. You can add storage and processing power as your data volume increases. Modern systems that process structured data can scale to several thousand TB of data.
Machine learning algorithms can analyze structured data and identify common patterns for business intelligence. You can use structured query language (SQL) to generate reports as well as modify and maintain data. Structured data is also useful for big data analytics.
What are some challenges of structured data?
While there are several advantages of using structured data for business, there are also some challenges.
The predefined structure is a benefit but can also be a challenge. Structured data can only be utilized for its intended purpose. For example, booking data can give you information about booking system finances and booking popularity. But it can’t reveal which marketing campaigns were more effective in bringing in more bookings without further modification. You’ll have to add marketing campaign relational data to your bookings if you want the additional insights.
It can be costly and resource-intensive to change the schema of structured data as circumstances change and new relationships or requirements emerge.
How is structured data different from unstructured data?
Unstructured data is information with no set data model, or data that has not yet been ordered in a predefined way. Here are common examples of unstructured data:
- Text files
- Video files
Enterprises are creating data at an exponential rate, and the vast majority of data—between 80-90%—is unstructured. As it is qualitative data, it requires different technologies and strategies to analyze effectively. For example, you store unstructured data in NoSQL databases and data lakes.
There are a number of key differences between structured and unstructured data.
Ease of analysis
One of the advantages of structured data is the ability of both people and computer programs to analyze the information. There are many tools for enterprises to analyze their structured data, and those tools are adept at providing insights and business intelligence. It’s significantly more difficult to analyze data that does not have a predefined data model, and far fewer proven tools in the market can do so.
Structured data is simple to search as it adheres to a number of predefined rules. By comparison, unstructured data lacks the order necessary to derive business insights using conventional data-mining techniques. Searching and analyzing unstructured data requires high levels of expertise and advanced analytical tools, such as natural language processing and text mining.
Given that the vast majority of data is unstructured, enterprises require more money, space, and resources to store it. In contrast, structured data has a more streamlined storage process. Structured and unstructured data are commonly stored in different environments, data warehouses and data lakes.
Structured data is generally stored in a data warehouse, which acts as a central repository for enterprise data. Data warehouses pull data from multiple structured sources, including databases and transactional systems. They are mainly used for data storage but are also utilized by businesses to analyze data and develop business intelligence. They can support large-scale data analysis by hundreds of business users.
A data lake is a central repository used to store raw, unstructured data. Data lakes are capable of storing unstructured data at scale. They are necessary for many modern enterprises that create large quantities of data daily. A data lake stores relational data from business applications and non-relational data from mobile applications, Internet of Things (IoT) devices, and social media.
What is the difference between structured, semi-structured, and unstructured data?
Semi-structured data sits between structured data and unstructured data. Semi-structured data cannot be considered fully structured data because it lacks a specific relational or tabular data model. Despite this, it does include metadata that can be analyzed, such as tags and other markers.
Semi-structured data is considered more straightforward to derive information and insights from than unstructured data. However, it does not have the completeness of information and adherence to a predefined data model in the same way structured data does.
Here are common examples of semi-structured data:
- Web files
- Zipped files
How can AWS help with structured data?
You can set up, operate, and scale relational databases in seconds with Amazon Relational Database Service (Amazon RDS). It’s a collection of managed services which can be managed on premises with AWS Outposts. These are the services included:
- Amazon Aurora with MySQL compatibility
- Amazon Aurora with PostgreSQL compatibility
- Amazon RDS for MySQL
- Amazon RDS for MariaDB
- Amazon RDS for PostgreSQL
- Amazon RDS for Oracle
- Amazon RDS for SQL Server
You can build web and mobile applications, move to managed databases, improve existing database efficiency, and break free from legacy databases.
Here are other things you can do with Amazon RDS:
- Migrate without rearchitecting applications
- Spend less time managing databases
- Cut capital and operational spending
- Focus on innovation
Join hundreds of enterprise customers using Amazon RDS by starting your free AWS trial today.
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