Structured vs Unstructured Data: Compared and Explained

Structured vs unstructured data in a nutshell

The key differences between unstructured data and structured data.

What is structured data?

The typical structured data example: Excel spreadsheet that contains information about customers and purchases.

What is unstructured data?

The travel agency Facebook post: an example of unstructured data.

The key differences between structured and unstructured data

Differences between structured and unstructured data in detail.

Data formats: few formats vs plethora of formats

Data formats.

Data models: pre-defined vs flexible

Storages for analytical use: data lakes vs data warehouses

Databases: SQL vs NoSQL

This is how SQL helps make queries.
The example of a NoSQL data structure.

Ease of search, analysis, and processing

Data nature: quantitative vs qualitative

  • classification or arranging stored items of data into similar classes based on common features,
  • regression or investigation of the relationships and dependencies between variables, and
  • data clustering or organizing the data points into specific groups based on various attributes.
  • data stacking or investigation of large volumes of data, splitting them into smaller items and stacking the variables with similar values into a single group, and
  • data mining or the process of detecting certain patterns, oddities, and interactions in large data sets to express possible outcomes in advance.

Tools and technologies

Structured data management tools.
  • PostgreSQL. It’s a free, open-source RDBMS that supports both SQL and JSON querying as well as the most widely used programming languages such as Java, Python, C/C+, etc.
  • SQLite. It’s another popular choice of an SQL database engine contained in a C library. It’s a lightweight and transactional system that doesn’t rely on a separate server process as it is rather inserted into the end-program.
  • MySQL. One of the most popular open-source RDBMSs that is fast and reliable. It runs on a server and allows for creating both small and large apps.
  • Oracle Database. This is an advanced database management system with a multi-model structure. It can be used for data warehousing, online transaction processing, and mixed database workloads.
  • Microsoft SQL Server. Developed by Microsoft, SQL Server is a reliable and functional relational database management system that makes it possible to store and retrieve data as per requests of other software applications.
  • OLAP applications. A unit of business intelligence (BI), online analytical processing (OLAP) stands for an advanced computing approach that answers multi-dimensional queries effectively and swiftly. OLAP tools allow users to work with data from different perspectives, because they combine data mining, a relational database, and reporting features. Apache Kylin is one of the most popular open-source OLAP systems. It supports large data sets as it is synced with Hadoop.
Unstructured data management tools.
  • MongoDB. This is a document-oriented database management system that does not require any rigid schema or structure of tables. It is thought of as one of the classic NoSQL examples. MongoDB uses JSON-like documents.
  • Amazon DynamoDB. Offered by Amazon as a part of their AWS package, DynamoBD is an advanced NoSQL database service for complete data management. It supports document and key-value data structures and is a good fit for working with unstructured data.
  • Apache Hadoop. This is an efficient, open-source framework used for processing large amounts of data and storing it on inexpensive commodity servers. Apart from being a powerful tool, Hadoop is also flexible as it does not require having a schema or a structure for the stored data. It helps with structuring unstructured data and then exporting this data to relational databases.
  • Microsoft Azure. Presented by Microsoft, Azure is a comprehensive cloud service for building and managing applications and services via data centers. Azure Cosmos DB is a fast and scalable NoSQL database that helps with storing and analyzing masses of unstructured data.

Data teams to handle data

Structured and unstructured data examples and use cases

How structured and unstructured data is used in different industries.

Structured data use case examples

Unstructured data use case examples

What is semi-structured data?

How data is organized in JSON.

The blurred line between structured and unstructured data



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