Mark Logic
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Mark Logic Flexible Data Model

MarkLogic® provides native storage for JSON, XML, RDF, geospatial, and large binaries (e.g., PDFs, images, videos). With this approach, it is easy to get all of your data in, and easy to make changes later on. Relational databases require predefined schemas and complex ETL to store data in rows and columns. With MarkLogic, you can load all of your data as-is, without

cumbersome traditional ETL processes. MarkLogic stores your structured and unstructured data—and your data and metadata— all together in the same database. And, if you need to add another data source with a different schema later on, that is okay. In the words of one MarkLogic customer, MarkLogic’s flexible data model “removes the shackles of relational technology.”

With MarkLogic’s multi-model approach, you have the power to store and manage all of your data. And, it is fully composable—, a single query can run across any number of data types. The table below summarizes the usage and description of each data type in MarkLogic.

At its core, MarkLogic stores data as JSON or XML documents. Among NoSQL databases, the document model is the most popular, and it helps solve many of the challenges with relational databases. Documents are ideal for handling varied and complex data, they are human-readable, they closely map to the conceptual or business model of the data, and they avoid the impedance mismatch problem that relational databases have.

Because the document model makes it possible to maintain and store multiple different schemas in the same database, data integration is easier and faster. And, with MarkLogic’s “Ask Anything” Universal Index, you can search and query across all of your data in real-time to see its structure and contents.


  • Semantics describes MarkLogic’s ability to store graph data as RDF Triples. Semantics enhances the document model by providing a smart way to connect and enhance the JSON and XML documents that MarkLogic stores, which is important for data integration and more powerful querying.Semantics also provides context for your data. For example, consider a database that has information about parts,and one part is listed with a size of “42.” But, where is the contextual information: What are the units of “42”? What is the tolerance? Who measured it? When was it measured? Who can see this data? That contextual information is the semantics of your data, and is easily stored in