What is structured data?
In big data, structured data is information organized in a predetermined way (a fixed format, data model or schema) within a record or a file.
Structured data is easy to use, store, analyze and manage. It’s easily understood by machines and searchable using simple queries and algorithms.
Did you know? Adding structured data to web pages boosts their search engine visibility.
Examples of structured data include phone numbers, customer names, addresses, zip codes, credit card numbers and dates.
And where does all this structured data come from 🧐? Website logs, clickstream data (website clicks), sales data, customer data, transactional records and financial data are examples of applications that generate structured data.
How can you store structured data?
Structured data is usually stored within spreadsheets, databases or data warehouses with a predefined length and format. Generally, it's maintained in relational databases.
Relational databases are based on the relational model of data—the data is stored in one or more tables of columns and rows, with a unique key identifying each row.
Rows are also called records or instances, whereas columns are also called attributes, features or independent variables.
A relational database usually contains a schema—a structural representation of information present in the database. A set of tables make up a schema.
Too much 😥? We got you covered. Here's an illustration to explain how schemas work.
The schema would define the tables, the fields in the tables and the relationships between the two.
How can you manage structured data?
Structured data can be managed using SQL (Structured Query Language)—a programming language used for querying data stored in RDBMS. Structured data can be easily analyzed using standard data analysis methods and tools.
The most popular RDBMS available in the market include Oracle, MySQL, Microsoft SQLServer, PostgreSQL, Microsoft Access, IBM DB2 and SQLite.
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