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In naming graph DBs one of the 10 biggest data and analytics trends of 2019, Gartner predicted that the category will grow a whopping 100 percent annually through 2022 “due to the need to ask complex questions across complex data, which is not always practical or even possible at scale using SQL queries.” Believe the prognosticators or don’t believe them, the movement is in fact here, and the DBs are being sold.
A good data point here is that graph databases are being used in multiple industries, including financial services, pharmaceutical, health care, telecom, retail and government.
A common question from the TigerGraph prospects who know Cypher and want to learn GSQL is “Do you have an example of the movie database from Neo4j?”. So, I thought it would be interesting to share an implementation of that movie database in GSQL as a learning resource. The idea is to provide a bridge for existing Cypher users to GSQL.
By Victor Moey, Originally posted on DZone
The graph database, very simply, is a database that recognizes the “relationships” between data to be as important as the data itself. A graph database is designed to hold data while not limiting it to a pre-established model. The data in such a database shows how each individual entity is connected with or related to others. A graph database “natively” embraces relationships while other databases compute relationships at the time of query using JOIN operations. A graph database stores its connections with the data in the model. In their early years, graph databases were “generally” regarded as a type of NoSQL or non-relational database, which were created to address the limitations of relational databases, but they’ve graphs have matured past such delineations and are considered their own type of innovative database technology.
Native parallel graph databases combine these three types of data in real time to deliver the four strategic imperatives. They deliver consistently high quality of care while controlling costs; detect and prevent waste and abuse; link public data with internal data to improve healthcare outcomes; and improve net promoter scores.
Rising healthcare costs is a complex issue with many causes. But native parallel graph databases are just what the doctor ordered to cure some of the contributing factors.
(Originally featured on FierceHealthcare)
This seventh DBTA 100 list spans a wide variety of companies that are each uniquely addressing today’s demands for hardware, software, and services.
Two recent, related partnerships between highly specialised “graph” database developers and public cloud platform providers underscores the importance of specialised databases capable of surfacing insights that would otherwise remain hidden within traditional database architectures.
Why is graph technology such a step forward? Virtually all existing risk assessment and monitoring systems are built on traditional relational databases, which store information such as counterparty, account, transaction, stakeholders, financial instruments and derivatives in separate tables, one for each type of business entity.
Machine learning has always been computationally demanding, and graph-based machine learning is no exception. With every hop, or level of connected data, the size of data in the search expands exponentially, requiring massively parallel computation to traverse the data. This is computationally too expensive for key-value databases which require too many separate lookups or RDBMS that struggle with too many slow joins. Even a standard graph database may not be able to handle deep link analytics on large graphs. A native graph database featuring massively parallel and distributed processing is needed.
As criminals deploy complex strategies and modern technology for tax evasion, graph databases can be used effectively by the IRS and other agencies all over the world to catch the crooks.
Byline by VP of Marketing