MarkLogic was named a Champion by Bloor Research in their newly released market comparison report on graph databases.
Bloor states in their associated InBrief that, “In short, if you want to leverage graph, relational and document data together, you should be looking at MarkLogic.” MarkLogic was evaluated against all of the leading graph database vendors. This includes both pure graph databases and other multi-model vendors with built-in graph processing – the category that MarkLogic fits in. The other Champions named in the report include Neo4j, Datastax, Ontotext, and Allegrograph.
What is also noteworthy is the vendors that are not included in the report. According to Bloor, “There is no doubt in our minds that graph databases are becoming more mainstream and that there are a broader range of use cases for which graph databases are being used. We expect this to continue. While it is encouraging to see vendors such as IBM add graph support in Db2, it only goes to validate the market.” The large mainstream vendors (Microsoft, IBM, Oracle, and SAP) are not included as they did not meet their criteria.
MarkLogic earned its high rating based on the strength of its Semantic graph capabilities that are tightly integrated into the core of MarkLogic Data Hub Service. With a built-in RDF Triple Store combined with native storage for JSON and XML, MarkLogic has distinct multi-model advantages compared to alternatives – and users of MarkLogic do not need to have expertise with graph database technologies.
Semantics is integral to MarkLogic Data Hub Service in the following ways:
- Data modeling – Data modelers get a simple interface to define their entity model and relationships before or after data is ingested. For that reason, customers often call their MarkLogic Data Hub an “Entity Hub.”
- Data curation – Semantics aids tasks such as deduplication (consider how there were 57 spellings of “Philadelphia” in the PPP loan data), data quality and disambiguation in data and metadata, and complex mappings even with nested entities.
- Data access – A graph view is one lens to view data. Semantics extends search to related and recommended terms (synonyms, sub classes, domain-specific ontologies). And, it helps run more sophisticated, faster, more scalable searches across structured and unstructured data.
- By way of example, it is a common pattern for customers to run document-style queries to pre-calculate sections of a graph, then run the graph query. With one of our customers in the airline industry, this multi-model approach enabled them to run analyses for 40,000+ aircraft using a 3-node MarkLogic cluster whereas the other vendor being compared required an estimated 300x more memory).
- Data intelligence – Ultimately, Semantics helps build a knowledge graph of data based on implicit and explicit assertions captured in metadata. By capturing the rich connections within metadata, users can ask harder questions such as “Where did this data come from? How was it curated?” and “What sources of data power my most critical business processes?”
As part of the report, Bloor provides a close look at the market segmentation, market trends, and metrics used for evaluation. They look at analytics, ease of use, features, language, operations, performance, and scalability.
Click here to get a copy of the Bloor report and we’ll also send you an email with the associated Bloor InBrief.