Unstructured Data Types

Make productive use of your unstructured data with MongoDB

According to a study by IDC, unstructured data accounts for over 95% of all digital content and is predicted to grow exponentially. Unstructured data refers to data that doesn’t fit neatly into the traditional row and column structure of relational databases.

Examples of unstructured data include:

  • Satellite images: Weather data or the data that the government captures in its satellite surveillance imagery
  • Scientific data: Seismic imagery, atmospheric data
  • Photographs and video: Security, surveillance, and traffic videos
  • Radar or sonar data: Vehicular, meteorological, and oceanographic seismic profiles
  • Internal Company Text: Text within documents, logs, survey results, and e-mails
  • Social media content: Data generated from the social media platforms such as YouTube, Facebook, Twitter, LinkedIn, and Flickr
  • Mobile data: Text messages, geospatial information
  • Website content: From any site delivering unstructured content, such as YouTube, Flickr, or Instagram

The explosion of unstructured data represents both a challenge and an opportunity for enterprises. Choosing the right technology solution to process, manage, and analyze data is critical to thriving in this digital age.

Many enterprises including a third of Fortune 100 companies choose MongoDB as the database that easily incorporates unstructured data. MongoDB’s document data model is particularly well suited for storing unstructured data because it stores all related data together within a document. The data doesn’t have to fit neatly into rows and columns as it does with traditional relational databases.

To learn more how MongoDB can help you make smart use of your unstructured data, read our white paper.

Companies ranging from startups to Fortune 500s choose MongoDB to build, scale, and innovate.

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