Your business changes. Your data changes. You can’t stand still while trying to model all that data into one relational schema. You need a multi-model database to handle all your data in one unified platform. In the words of one MarkLogic customer, MarkLogic’s flexible data model “removes the shackles of relational technology.”
At its core, MarkLogic is a multi-model database. 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. Load all of your data as is — structured and unstructured data (and your metadata!) — without cumbersome ETL processes. If you need to add another data source or make changes to your schemas later on, go on! It’s flexible. It’s fast. It’s iterative.
Documents are fantastic for storing entities. But, when it comes to relationships in data, a graph database is best. A graph database is designed to store and manage relationships among people, customers, providers, or anything else.
MarkLogic has a built-in RDF Triple Store (a type of graph database) for storing and managing semantic data. We call this capability MarkLogic Semantics. 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.