Advantages of graph databases: Easier data modeling, analytics
In his role as principal data scientist at consulting firm Booz Allen Hamilton Inc., Kirk Borne sees the world in terms of data connections. “Life is about who is connected to whom and what is connected to what,” Borne said, and he pointed to graph databases and graph analytics applications as new ways to capitalize on such connections.
That’s because graph databases, a form of NoSQL software, document the connections between data points quite different compared to mainstream relational databases. Graph systems represent data not as elements in tables, but as nodes linked to one another by edges with a set of properties that delineate the relationship between nodes.
Therefore, one of the advantages of graph databases is they allow data analysts to navigate through data sets without the need to create and run complex queries to join combinations of tables together, as in the relational model.
“Graphs make more sense from a data discovery perspective,” Borne said. When graph algorithms and analytics tools are applied to data sets, basic functions, such as clustering, partitioning, search and estimating the shortest path between nodes, disclose patterns in the data, according to Borne and others.
Graph use cases on the rise Borne noted that the graph approach underlies some bellwether online applications. That includes the page ranking system in Google’s search engine and its Knowledge Graph, which pulls together factual data from various online sources. Facebook and LinkedIn also use graphs to map their networks of friends and connections. In addition, graph analytics is used in online recommendation engines. These applications aren’t implemented in graph databases in every case; graphs can be built, stored and managed in various platforms, including relational ones. But interest in graph database technology is growing among vendors and IT teams alike, with fraud detection, cybersecurity, text analysis, data catalogs, master data management and scientific research among the uses now in production. Today, graph tools have more rich data to do discovery against. The rising tide of big data is one of the factors prompting more users to consider the advantages of graph databases and graph data modeling approaches. “Today, graph tools have more rich data to do discovery against,” Borne said. For example, Neo4j Inc.’s namesake graph database provides a platform to collect and share a variety of genetic data and other information related to diabetes for Alexander Jarasch, head of data and knowledge management at the German Center for Diabetes Research in Munich. Known by the acronym DZD in German, the center is using Neo4j as part of its efforts to create new therapies for diabetes patients and find ways to prevent the disease, Jarasch said. He started scripting and prototyping work with the graph database in April 2017 and was joined in that effort by his two other team members last year. This visualization created with graph database vendor Neo4j’s Bloom data exploration software highlights connections among companies, people and cities.
After more than 10 years of working in bioinformatics, Jarasch developed an aversion to relational databases — or at least the data joins that so often are central to relational queries. “I hate joins,” he said. “When you have data scattered over tables and you look for insights, it gets complicated.” Jarasch and his colleagues are looking to use the Neo4j database to enable easier sharing of diverse data within the DZD, which comprises a number of independent research organizations. The data comes from a mix of hospitals, labs and other sources, according to Jarasch; some of the data is on humans, and some is on test animals. “Everybody has their data in silos,” he said. “They exchange data, but there’s no overarching way to connect the data.
