Graph databases have moved from a topic of academic study into the mainstream of information technology in the last few years. Now CIOs are confronted with the need to better understand:
- What business problems do graph databases address well?
- What advantages do graph databases offer over widely-implemented relational databases?
- What trends have led to the increased prominence and adoption of graph databases?
- What issues emerge as graph databases are introduced into an existing application portfolio?
A graph database (GDB) uses graph structures to represent and store data. Graph databases emphasize relationships among data entities.
DBMS developments
First a bit of history: To improve data management and data processing as data volumes grew, database management systems (DBMS) emerged as a separate software layer between the operating system and the application program in the 1960s.
By the 1980s the relational DBMS had become and has remained the principal DBMS. The 2000s saw the emergence of XML databases, NoSQL databases, and the idea that databases didn’t need to be tightly structured in a purely tabular form.
During the 2010s, databases that support the JSON open standard file format gained traction. We also saw the rise and ultimate fall of Hadoop, a software framework for using highly distributed storage to process big data.
A note of caution: Graph databases are not a substitute or an alternative for relational databases. The two types of databases fulfill different data processing and application objectives. This article focuses on describing the data and applications where graph databases can be a superior solution.
Data volume explosion
Fast forward to today: Data volumes are continuing to explode exponentially. The vast data volumes are being generated by many sources including:
- Internet of Things (IoT). The explosion of industrial and consumer devices, or things, that monitor the performance of almost everything and IoT devices that are replacing analog data recording devices, all generate huge data volumes.
- Digitalization of society. The most obvious examples are the vast volume of digital data available on the web and its consumption by billions of people.
- Graphic and video data types. Originally, data meant letters and numbers only. Introducing graphics and videos has added many orders of magnitude more data.
- Digital transformation of businesses and government. Most organizations are actively working to enhance application functionality and eliminate the remaining bits of paper and Excel workbooks that exist between their systems.
- Voracious demand for data analytics. The demands of data analytics triggered by the move toward more data-driven organizations have added significant data volumes.
Graph database opportunities
Today’s problem: The many DBMS advances plus huge improvements in computing infrastructure performance, introduced over many decades, are nonetheless straining or failing to handle these vast data volumes.
Today’s solution: Applications that access graph databases can solve various types of problems that are creating frustrations at the enterprise level. Examples of these applications include:
- Artificial intelligence.
- Computing infrastructure monitoring.
- Customer 360 interaction analysis.
- Fraud detection.
- Knowledge-based.
- Metadata management.
- Master data management.
- Natural language processing.
- Recommendation engine.
- Social media influencer analysis.
These applications benefit from using graph databases because they:
- Deliver excellent performance for complex data analytics.
- Simplify data ingestion and integration from diverse sources.
- Manage vast data volumes reliably.
At the recent Collision from Home virtual conference, Javier Ramirez, Senior Developer Advocate, Amazon Web Services, described how graph databases are superior for managing highly interconnected data, and for quickly producing concise results for complex queries. AWS offers the Neptune graph database service. He said that “Neptune addresses the graph database issues that many end-users encounter. These issues include lack of scaling, non-existent high availability and uneven support for open standards.”
For more information about graph databases and vendor-specific assessments, please consult the Gartner Magic Quadrant for Data Management Solutions for Analytics.
What strategies would you recommend to successfully guide the selection and implementation of a graph database? Let us know in the comments below.
