Structured data, as the name implies, indicates datasets that have a defined organizational structure such as Microsoft Excel or CSV files. In pure database terms, the data should be representable using a schema. As an example, the following table representing the top five happiest countries in the world published by the United Nations in its 2017 World Happiness Index ranking would be an atypical representation of structured data.

We can clearly define the data types of the columns--Rank, Score, GDP per capita, Social support, Healthy life expectancy, Trust, Generosity, and Dystopia are numerical columns, whereas Country is represented using letters, or more specifically, strings.

Refer to the following table for a little more clarity:

World Happiness Report, 2017 [Source: https://en.wikipedia.org/wiki/World_Happiness_Report#cite_note-4]

Commercial databases such as Teradata, Greenplum as well as Redis, Cassandra, and Hive in the open source domain are examples of technologies that provide the ability to manage and query structured data.

Unstructured data consists of any dataset that does not have a predefined organizational schema as in the table in the prior section. Spoken words, music, videos, and even books, including this one, would be considered unstructured. This by no means implies that the content doesn’t have organization. Indeed, a book has a table of contents, chapters, subchapters, and an index--in that sense, it follows a definite organization.

However, it would be futile to represent every word and sentence as being part of a strict set of rules. A sentence can consist of words, numbers, punctuation marks, and so on and does not have a predefined data type as spreadsheets do. To be structured, the book would need to have an exact set of characteristics in every sentence, which would be both unreasonable and impractical.

Unstructured data can be stored in various formats. They can be Blobs or, in the case of textual data, freeform text held in a data storage medium. For textual data, technologies such as Lucene/Solr, Elasticsearch, and others are generally used to query, index, and other operations.

Semi-structured data refers to data that has both the elements of an organizational schema as well as aspects that are arbitrary. A personal phone diary (increasingly rare these days!) with columns for name, address, phone number, and notes could be considered a semi-structured dataset. The user might not be aware of the addresses of all individuals and hence some of the entries may have just a phone number and vice versa.

Similarly, the column for notes may contain additional descriptive information (such as a facsimile number, name of a relative associated with the individual, and so on). It is an arbitrary field that allows the user to add complementary information. The columns for name, address, and phone number can thus be considered structured in the sense that they can be presented in a tabular format, whereas the notes section is unstructured in the sense that it may contain an arbitrary set of descriptive information that cannot be represented in the other columns in the diary.

In computing, semi-structured data is usually represented by formats, such as JSON, that can encapsulate both structured as well as schemaless or arbitrary associations, generally using key-value pairs. A more common example could be email messages, which have both a structured part, such as name of the sender, time when the message was received, and so on, that is common to all email messages and an unstructured portion represented by the body or content of the email.

Platforms such as Mongo and CouchDB are generally used to store and query semi-structured datasets.