What type of data is Azure Cognitive Search optimized to handle?

Azure Cognitive Search is designed to handle unstructured or semi-structured data effectively, making it a powerful tool for indexing and searching diverse datasets. Unlike structured data, which is highly organized and easily searchable (like data in relational databases), unstructured data lacks a predetermined format and can include text files, images, and web pages. Semi-structured data, on the other hand, may contain some organizational properties but isn't as rigidly structured as traditional databases.

The capability of Azure Cognitive Search to process and extract insights from unstructured or semi-structured data enables organizations to index content from various sources, such as documents, emails, and web content, thereby facilitating powerful search functionalities and improving the accessibility of information. This versatility is particularly valuable in enterprise search scenarios, where information is often scattered across unstructured formats.

In contrast, the other types of data mentioned, such as only structured or numerical data and real-time streaming data, do not align with the primary focus and functionalities of Azure Cognitive Search. Therefore, the platform's optimization for unstructured and semi-structured data allows users to leverage its full potential to enhance search experiences and knowledge discovery across multiple data types.