Comparison with MongoDB:
MongoDB provides all aspects of data storage for online retail stores. It can categorize data based on merchandising, content, inventory, customer, channel, sales and fulfillment, insight, and social. Out of these merchandising, inventory, customer, and insight are the most heavily used for peak holiday sales season. In addition, supply chain management systems and data warehouses can also integrate well with this database.
MongoDB is not a traditional Master data management product but it addresses most of the requirements. A traditional Master Data Management has a well-known organization of catalog with support for hierarchy and dimensions. MongoDB organizes the catalog in the form of Items, Pricing, Promotions, Variants, and Ratings, and Reviews. In JSON this appears as nested fields and are pre-joined into objects. The objects live in the cache as well. A search engine provides a search over the catalog. Functional data access is provided by the Product API. The API and the engine separately cover all operations on the catalog. The API can then be used by downstream units such as Online Store, Marketing, Inventory, SCMS, and other APIs. The Search engine is built on Lucene/Solr Architecture A Lucene index keeps track of terms and their occurrence locations, but the index needs to be rebuilt each time the catalog changes. The Product API can retrieve results directly from the catalog or via the search engine. In both cases, the customer only issues a single query.
MongoDB offers the catalog as a one-stop-shop from its store. There are no sub-catalogs or fragmentation or ETL or message bus. It is highly available to Application servers, API data, services, and web-servers. It is also available behind the store for supply chain management and data warehouse analytics which typically have their own analysis stacks. The catalog is available for browsing as well as searching via the Lucene search index. Queries can be written with keywords to narrow down the results from the catalog. MongoDB allows geo-sharding with persisted shard ids or more granular store ids for improving high availability and horizontal scalability. It provides local real-time writes and tuned for the read-dominated workload. It performs bulk writes for a refresh. The relational DB stores point in time loads while overnight they are pushed to catalog information management and made available for real-time views. The NoSQL powers insight and analytics based on aggregations. It provides a front-end data store for real-time queries and aggregations from applications. This comes with incredible monitoring and scaling
Both MDM and MongoDB Catalog store hierarchy and facet in addition to Item and SKUs as a way of organizing items.
MDM providers like Riversand, on the other hand, offer rebuildable catalog via change data capture, .Net powered comprehensive web services, data as a service model and reliance on traditional relational databases only
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