Data is the lifeblood and foundation of product lifecycle management (PLM) solutions. In a world where data management is quickly becoming an integral part of every business, thinking about data and data management is one of the first priorities.
Although earlier PDM/PLM systems made attempts to develop proprietary data management solutions, later standardization of data management and alignment with RDBMS and SQL made them de-facto standards in the industry. All established PLM platforms today are running on one of the popular databases (Oracle, Microsoft SQL Server, or similar open-source equivalents such as PostgreSQL). For many years, data management in PLM was confined to the capabilities of SQL or special SQL variations supported by specific databases. A typical PLM data management architecture is an object modeler on top of SQL database.
Data Management is Changing
The data management architecture is changing these days. Over the last 20 years, we’ve witnessed a Cambrian explosion in data management technologies and systems. Much innovation has emerged from the development of global web platforms, the necessity to handle big data, and the opportunities to optimize database management systems and data analytics in cloud and SaaS-based platforms. The introduction of SaaS CAD and PLM systems, with no need to obtain IT approval for database decisions, conceals data management from the customer and opens up the opportunity to choose the best tool for the job. It brings forth the ideas of polyglot persistence and multiple databases. A modern data management stack encompasses many databases and systems to control and manage data in various forms. Modern PLM systems go beyond relational databases and relational database systems. Check out my article “PLM and Data Management in the 21st Century” to learn how multiple database management systems can be used in PLM development. Current data architectures surpass relational databases and allow for storing data in the manner needed to support specific types of information and operations.
Why Graph Database are important for PLM
In my earlier articles, I wrote about my take on how graph databases and graph models can change the PLM architecture landscape and provide a new way for data modeling and data management. Check out some of my articles with the following links:
Graphs Maps The Future of PLM via Engineering.com
The importance of knowledge graphs for the future of PLM platforms
Will Graph database become a future PLM silver bullet?
Why do you need Graph Model to embrace PLM complexity?
Network-based solutions, graph database and future PLM architecture
Overall, I see graph models as offering a flexible foundation for addressing the complexity of data management in PLM platforms. This encompasses managing intricate semantic relationships, supporting data management flexibility, and facilitating change management systems. Unlike relational database systems, which lack the capacity to deliver such levels of power and flexibility, a combination of data management systems could herald a potent transformation for PLM.
From Neo4j to Open Cypher and GQL
My attention was caught by Neo4j blog speaking about GQL. Check the article – ISO GQL: A Defining Moment in the History of Database Innovation. It is a very interesting moment for the future of database development and it will have an impact of PLM development. Here is the passage.
Last Friday, ISO published a new database query language: ISO GQL. It’s a peer language to SQL and the first new ISO database language since 1987 — when the first version of SQL was released.
The GQL standard is for and about graphs. Graphs are a way of working with data that you already have. They excel with exactly the type of data that the world is creating more of, building applications around, and increasingly finding ourselves needing to analyze and make sense of.
Database management systems (DBMSs) that work with your data as a graph are called graph databases. The ISO GQL (Graph Query Language) standard uses a particular type of graph called a property graph. Its design dates back over two decades, and it has gained significant popularity in recent years across a wide range of applications – spanning a multitude of use cases in just about every industry.
Having an international standard for graph databases adds immense value to a landscape where data is increasingly dynamic and interconnected. That ISO has invested more than five years in creating this standard says something about the importance of this technology.
Standards play a crucial role in enhancing the development of systems, and when it comes to data management, they can mark significant turning points. Until now, choosing a specific graph database has often resulted in proprietary development of data connections and the use of specific query languages (e.g., Cypher in Neo4j). I’ve been watching the development of openCypher and expected ISO standard to come. Introducing a standardized query language such as GQL can usher in a new era for graph model development and support in graph databases.
What is my conclusion?
PLM database management system development expands from the usage of relational databases only towards exploring new data model paradigms supported by a large number of new data management solutions. It is important to support complex data structures and provide effective data management for PLM tools. Graphs help to bring relevant data, perform data analysis and organize database records in a much more flexible and efficient ways. Graphs can contribute to the development master data management and helps to establish dynamic and connected single source of truth for PLM architectures. In my view, graph data models and graph databases will play a key role in the future of PLM development, introducing a new level of flexibility and efficiency in product lifecycle data management. The introduction of GQL as an ISO standard is another confirmation of the power of graph database systems and graph models, and also a statement to support the adoption of these models for software developers. It is a note to all PLM data architects – if graph databases are not in your toolbox, now is a great time to close the knowledge gap.
Just my thoughts…
Best, Oleg
Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital-thread platform with cloud-native PDM & PLM capabilities to manage product data lifecycle and connect manufacturers, construction companies, and their supply chain networks. My opinion can be unintentionally biased.
