Some things to consider when opting for graph databases in engineering applications.
Graph databases have moved from a topic of academic study into the mainstream of information technology in the last few years. Now engineers want to better understand:
- What advantages do graph databases offer over widely implemented relational databases?
- How can graph databases advance digital transformation initiatives?
- How do graph databases enhance engineering applications?
- What business and technical problems do graph databases handle better than the alternatives?
- How can graph databases increase the value engineers can derive from their data?
What is a graph database?
A graph database (GDB) uses graph structures to represent and store data. Graph structures consist of data tables for entities, called vertices, and relationships, called edges. Graph databases physically store pointers for the relationships between the entity values. This feature differentiates graph databases from relational databases, which establish relationships by performing joins based on foreign key values.
Engineers are attracted to graph databases to meet big, complex data challenges that traditional relational and more recent NoSQL databases cannot conquer. High-volume data challenges arise frequently in organizations with a high degree of digital transformation.
Graph database advantages
Graph databases offer advantages over the more widely used relational or specialty NoSQL databases based on their ability to persist relationships among entity values. This technical feature leads to the following benefits:
- Superior query speed, which is especially noticeable as query complexity increases.
- Ability to describe real-world relationships accurately. This accuracy improves developers’ understanding of the database and simplifies the implementation of schema changes.
- Flexibility in revising data structures. This flexibility reduces the cost and disruption of modifying the database and application programs as changes are required.
How graph databases advance digital transformation
Graph databases’ advantages are realized when digital transformations use them for their digital datastores.
Here’s how. Digital transformation typically requires the following tactical steps after the strategy has been defined:
- Transforming and integrating data from disparate data sources.
- Scanning and digital conversion of paper documents.
- Structuring unstructured digital data.
- Cleaning and augmenting digital data.
- Integrating digital data from external data sources.
- Implementing data analytics and visualization software.
Engineers who employ graph databases can perform these digital transformation steps to improve digital data accessibility faster and more cheaply.
Graph databases enhance engineering applications
The benefits of graph databases apply to many of the applications engineers work with routinely. Whenever engineers can better understand relationships among parts, components, subassemblies, tasks, and work processes throughout the engineering life cycle, they can:
- Improve quality.
- Identify and plan to mitigate risks.
- Reduce elapsed time and delays.
- Reduce costs or at least minimize expected increases.
- Anticipate future problems and identify alternative courses of action.
Managing these relationships at a detailed level and making them easily visible to engineers is precisely the value that graph databases offer.
Graph database opportunities
Data volume explosions and increasing application sophistication trigger the use of graph databases. The many DBMS advances of the past, plus considerable improvements in computing infrastructure performance introduced over many decades, are nevertheless straining or failing to handle these increasing demands.
Applications that access graph databases can solve these lack-of-scale problems that create frustrations. Examples of applications which engineers use regularly and benefit from graph databases include:
- Artificial intelligence.
- Asset management.
- Automation and computer-aided manufacturing.
- Computer vision.
- Computer-aided design.
- Construction management.
- Defect tracking.
- Digital twin and simulation.
- Inventory and warehouse management.
- Maintenance management.
- Manufacturing process optimization.
- Product lifecycle management.
- Quality control.
These applications benefit from using graph databases because they can:
- Deliver excellent performance for complex data analytics.
- Simplify data ingestion and integration from diverse data sources.
- Manage vast data volumes reliably.
- Offer high application availability,
- Support open standards for query and update syntax.
When is a graph database superior?
Graph databases are suited to specific application characteristics. They are not a substitute or an alternative to relational or other databases. The various types of databases fulfill different data processing and application requirements.
Graph databases are superior when working with less structured data, many relationships and processing modest numbers of large transactions with reasonable response times. Large transactions typically access multiple tables for many or even all entity values. Relational databases are superior when working with highly structured data, few relationships and processing large numbers of small transactions with fast response times. Small transactions typically access a small number of tables for a single entity value.
Graph databases are superior when the data and structure are not well understood at database design time and are expected to change. Relational databases are superior when the data and structure are well understood at database design time and are not likely to change much. The difference is that the database schema of graph database applications can be revised without taking the application offline. That’s not possible with relational databases.
Graph databases increase the value of data
Organizations increasingly understand that their data contains more value than was previously recognized. Graph databases allow engineers to extract that value when they:
- Examine dynamic, complex or unusual relationships between data. This capability is particularly useful for analyzing root causes, understanding component interactions and building visibility of supplier performance.
- Can understand context and relevance more easily. Context describes the relationship among individual pieces of data. Relevance defines the usefulness of pieces of data to making specific decisions. This capability is advantageous for evaluating design alternatives, avoiding distractions and confusion in decision-making and responding to supply chain disruptions.
- Can easily search their organization’s intellectual property, typically stored as unstructured data in documents. This capability is helpful for opportunity discovery or problem analysis.
Graph databases add value for engineering applications when they manage large volumes of data with significant numbers of relationships among entities. Examples include product and component codes, material types and finish codes or performance specifications.
