What is structured data?
Structured data refers to datasets with strong and consistent organization. Structured data is managed by structured query language (SQL), by which users can easily search and manipulate the data.
What is an example of structured data?
Structured data is organized into rows and columns with known and predictable contents. Each column contains a specific data type, such as dates, text, money, or percentages. Data not matching that column's data type is rejected as an error. Relational database tables and spreadsheets typically contain structured data. A higher semantic level of structure combines master data and historical data into a data model. Data model subject areas include topics such as customers, inventory, sales transactions, prices, and suppliers. Structured data is easy to use, and data integrity can be enforced. Structured data becomes big data as huge amounts of historical facts are captured.
What are the benefits of structured data?
Structured data is easily understood by machine learning algorithms, making manipulation simple and fast. It’s also easy to query by users, who are able to access, understand, and interpret the data.
Structured data tools
Some of the commonly structured data tools are relational database management systems (RDBMS). These include:
- SQLite. A lightweight, serverless embedded database designed to be easily integrated into applications and software without the need for a separate database server or complex setup procedures.
- MySQL. A reliable, fast, open-source RDBMS widely used for both small, web-based applications as well as business-critical enterprise systems. It’s known for its compatibility with industry standards and other RDBMS systems, and can be used with various programming languages, including PHP, Python, and Java.
- PostgreSQL. Powerful, highly extensible, and versatile, PostgreSQL supports a wide range of data types, including integer, text, boolean, date and time, JSON, XML, and even user-defined data types. This flexibility is suitable for advanced data modeling.
- OLAP. While not an RDBMS, online analytical processing (OLAP) facilitates multidimensional data analysis. These systems are designed for complex and high-performance querying, reporting, and data visualization, particularly in the context of business intelligence and data warehousing.
Features of structured data
- Consistent data types. Each data element in structured data has a well-defined data type across all values. Examples include integers, floating-point numbers, strings, and dates. This ensures data consistency and makes storage and processing highly efficient.
- Queryable. Structured data can be easily queried and analyzed in relational databases using standardized query languages, such as structured query language (SQL). Queries can filter, sort, and aggregate data for various uses.
- Indexing. Structured data is typically organized and indexed to optimize data retrieval. This accelerates data searches and data access.
- Historical data. Many structured data systems store historical records, allowing users to access and analyze sets of data to track changes and trends over time.
- Reporting and analysis. Structured data is well suited for reporting and analysis tasks, as well as data manipulation, which can enhance business intelligence and decision-making.
Structured vs. unstructured data
While structured data deals with highly organized quantitative data managed with SQL databases, unstructured data deals with qualitative data using no predefined data model, which is best managed in NoSQL databases (such as MongoDB).
All important business processes and decisions depend on structured data. It is the foundation of data warehouses, data lakes, and applications. When integrated into a data model, structured data provides exponential business value.
How Teradata can help with structured and unstructured data
Quickly analyze your structured data in a flexible work environment, streamline data processes, and gain deeper insights into your customers’ behavior by leveraging the powerful capabilities of a data warehouse with Teradata VantageCloud.
Data warehouse
Our efficiently designed data warehouses can support your goals and drive better business.
- Share information across functional units. Bring together various sources of data into one place to reduce data silos and ensure data consistency.
- Get vital business answers quickly and accurately. Efficiently deliver answers to your most complex business questions to help improve decision-making and strategy.
- Provide a single version of the truth. Everyone in an organization can work from the same information to draw conclusions about the state of the business.
Data lake
Efficiently leverage your structured, unstructured, or semi-structured data with a data lake.
- Eliminate silos. Store data in its original format from anywhere—all in a single, secure, managed environment that’s easily accessible across the organization.
- Fuel innovation. Streamline cross-team collaboration, uncover insights, and fuel AI-powered innovation and automation faster than ever before.
- Adapt fast to market shifts. Explore and experiment quickly with vast amounts of data using industry-leading tools and solutions.
Lakehouse
Experience the benefits of both a data warehouse and a data lake with a lakehouse. A lakehouse combines a “single source of truth” for your data along with advanced analytics and machine learning capabilities to maximize business value.
- Connect all your data. Automatically integrate new data sources—regardless of type—without altering them to fit formats or schemas.
- Easily access all your data. Store and process data in one platform that's accessible to innovators across the enterprise.
- Drive growth, fast. Accelerate breakthrough insights to power innovation while boosting data security, governance, and versioning.
Additional Resources