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Document querying is a software development technique used to retrieve and manipulate specific information from a documentation system, database, or other structured data source. The query itself is a set of instructions or conditions that specify what information to retrieve, how to filter or aggregate the data, and possibly even perform some calculations or manipulation on the results.

Document querying can be applied in various domains, including business intelligence, content management systems, and knowledge management software. The main goal of document querying is to provide an efficient and flexible way to access and process large amounts of data, often with the help of powerful search technologies and features such as filtering, sorting, and grouping.

There are several types of document querying methods and technologies, including:

1. Structured Query Language (SQL) for relational databases, which provides a standardized way to define database structures and execute queries on large datasets.

2. NoSQL querying and indexing techniques, used to optimize queries for non-relational databases, such as key-value stores and document-oriented databases.

3. Full-text querying and searching, which enables users to search and retrieve documents by content, often using techniques like keyword searches, stemming, and lemmatization.

Document querying has various applications in real-world scenarios, including:

1. Information retrieval and search engines, which often use sophisticated document querying technologies to return accurate and relevant results to users' queries.

2. Business intelligence and data analytics, which can use document querying to extract insights and patterns from large datasets and inform decision-making.

3. Content management systems and knowledge management software, which can apply document querying to provide users with personalized and relevant recommendations.

In summary, document querying represents a set of techniques and technologies aimed at efficiently and effectively retrieving and manipulating information from a wide range of data sources and structures.