Where is unstructured data stored

That said, email metadata affords it some structure, and explains why email is sometimes considered semi-structured data. Text files: This category includes word processing documents, spreadsheets, presentations, email, and log files. Social media and websites: Data from social networks like Twitter, LinkedIn, and Facebook, and websites such as Instagram, photo-sharing sites, and YouTube.

Mobile and communications data: Text messages, phone recordings, collaboration software, Chat, and Instant Messaging. Media: Digital photos, audio, and video files. Here are some examples of unstructured data generated by machines: Scientific data: Oil and gas surveys, space exploration, seismic imagery, and atmospheric data.

Digital surveillance: Reconnaissance photos and videos. Satellite imagery: Weather data, land forms, and military movements. Unstructured data — comprising most other types — exists in formats such as audio, video, and social media postings, and is not easy for conventional tools to search.

Relational databases handle structured data, and just about all other kinds of systems can house unstructured data. Typical unstructured use cases are media viewing and editing tools, presentation software, and word processing.

There is also a third category called semi-structured data. While not stored in relational databases, this type of information has some organizing properties, making it easier to parse and analyze.

Specifically, semi-structured data contains internal tags and markings that allow for grouping and hierarchies. Email is a common semi-structured data application. While detailed email analysis requires sophisticated tools, its native metadata allows for basic classification and keyword searches.

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Unstructured Data. Nature of data. Usually quantitative. Usually qualitative. Data model. Data format. A limited number of data formats are available. A huge variety of data formats are available for unstructured data. SQL-based relational databases are used. NoSQL databases with no specific schema are used. Very easy to search and find data within the database or data set. Very easy to analyze, given the quantitative nature of data.

Very difficult to analyze, even with existing software tools. Storage method. Data warehouses are used for structured data. Data lakes are used to store unstructured data. The simplicity and scalability of a single global namespace combined with a simple stateless data management protocol for example, Amazon S3 and Swift help organizations deliver a scalable and collaborative environment across geography, organization, and application boundaries.

With StorageGRID, you can build a massive multilocation single namespace, and you can also integrate a unique information lifecycle policy into that data. NetApp uses cookies and similar technologies to improve and customize your online experience. By closing this banner or by browsing this site, you agree and accept the use of cookies. To learn more, please refer to our recently updated Privacy Policy. Quick Links. Hybrid Cloud. Data Storage. Data Protection. Data Management. Enterprise Applications.