Every lab that uses software like a laboratory information management system (LIMS) is using a database to store and access lab data. However, what lab managers might not be aware of is that the underlying type of database—relational or non-relational—can impact how effectively a lab can innovate and scale.
Labs that aim to rapidly put new assays into production and iterate on them need a database that supports two primary functions: storage of data with its context (metadata) and easy reuse and searchability. However, most, if not all, LIMS on the market today use a relational database, which doesn’t support these critical functions. Studies show that graph databases, a type of non-relational database, are much more suitable for biomedical applications.
Relational vs Non-Relational Databases
Databases generally fall into two main categories: relational (SQL) databases and non-relational (NoSQL) databases. Each type has distinct structures and use cases.
Relational ("SQL") Databases
Relational databases consist of multiple related tables, where data is stored in rows and columns. They use structured query language (SQL)—a technology developed in the 1970s—to read and modify data. Popular examples include:
- PostgreSQL
- Microsoft SQL Server
- Oracle Database
- MySQL
These databases are widely used for applications requiring structured data storage, strong consistency, and complex querying.
Non-Relational ("NoSQL") Databases
Unlike relational databases, non-relational databases do not rely on tables. Instead, they offer a more flexible data structure, making them suitable for applications where scalability and varied data formats are key. Common types include:
- Document datastores
- Column-oriented databases
- Key-value stores
- Graph databases
Graph databases are further divided into two categories:
- RDF Graphs (e.g., OntoText GraphDB) – These conform to W3C standards and support RDF (Resource Description Framework).
- Property Graphs (e.g., Neo4j) – These do not support RDF and are generally less precise but more flexible.
Each type of database serves a different purpose, and choosing the right one depends on the structure and needs of your data. If you’re interested in learning more about RDF vs. Property Graphs, check out this Oracle article.
The Limitations Of SQL Databases For Modern Labs
In a relational database, you must perform data modeling in order to set up database tables for the specific types of data to be stored. You’ll need to know upfront what types of data you want to store and what types of queries you’ll want to perform. If you need to restructure the data at any point, you have to perform a database migration—a process that becomes increasingly high-risk as the volume of data grows because every new addition to a table requires a corresponding change in the software code. The more changes you make, the more chance you have of introducing an error.
As your business evolves, what you store is likely to change based both on what you want to query and the new products your lab wants to create. In our experience, labs tend to use a lot of unstructured and user-defined data, which is not easy to deal with in a structured database table.
Non-relational databases are less likely to need major database restructuring due to the inherent flexibility of their structures and the software applications that use them. For example, each piece of data is stored with its own data type, unlike in a relational database, where entire table columns need to be a uniform inferred type.
What Is An RDF Graph Database?
RDF graph databases use a data model that consists of:
- Nodes representing entities, such as a person, sample, or reagent.
- Edges representing relationships between entities. For example, a sample is processed by a person.
This data model is much more flexible than a table, as it does not constrain the type of data that can be added to the graph. It also records the relationships between entities, which is a form of human- and machine-readable metadata not explicitly stored in relational databases. We chose an RDF graph database for Labbit due to three primary benefits.
3 Advantages Of A Graph Database
RDF graph databases offer clinical labs a number of benefits compared to other types of databases. We’ve distilled these down to three main ones:
1. More Flexibility & Agility
Graphs can evolve as your business and data requirements change. They allow you to include primary entities of any kind without adding technical debt in the data model, which convolutes reporting and thus necessitates ETLs.
2. Accurate Data, Faster Queries
Easier querying and more precise data capture are possible because graph-stored data more accurately represents reality than the normalized forms required by relational databases. When you need information from the database, the graph can quickly and, with context, tell you exactly what you need to know.
3. Adaptable Data Models For Your Lab
The ability to create and update the data model within your LIMS to match the evolving ontology your laboratorians hold in their mind about the lab rather than forcing them to adapt to the software’s narrowly defined view of it.
Enhancing Machine Learning & Data Interoperability
This ability supports two very useful applications for labs:
- MUCH faster knowledge inference for machine learning applications.
- Improved data shareability by enabling data interoperability. Paired with the right software and planning, an RDF graph enables a lab’s data to be stored, maintained, and shared in accordance with the FAIR data principles. Each piece of data includes rich metadata and has a unique identifier (in an RDF graph database, this is an internationalized resource identifier or IRI). These identifiers let a lab make connections to other sources of data and share the data with external users.
Here’s a quick comparison between relational and graph databases.
Relational Databases
Relational databases use foreign keys between tables to infer relationships, while graph databases store relationships explicitly as data between nodes. This makes relational databases more rigid, requiring a predefined schema and potentially complex migrations to accommodate new data types.
Graph Databases
Graph databases offer greater flexibility, allowing new data to be added without altering the schema. When it comes to complex querying, relational databases rely on joins across multiple tables, which can be slower and require deep knowledge of the schema. In contrast, graph databases enable faster, more intuitive queries by following direct connections between nodes, making it easier to perform ad-hoc queries without prior knowledge of the data model.
FAQs - Graph Databases
Can a graph database handle large volumes of data in a lab?
Yes, graph databases are designed to manage and query large datasets efficiently. Their structure allows for the rapid processing of complex relationships, making them suitable for labs handling extensive volumes of interconnected data, such as experimental results, samples, and equipment metadata.
How does a graph database improve collaboration in labs?
Graph databases inherently store data in a FAIR (Findable, Accessible, Interoperable, Reusable) manner. This structure ensures that data is not only easily discoverable and accessible across teams but also reusable for various research purposes.
By visualizing connections between datasets, graph databases simplify collaboration, allowing researchers to seamlessly access, understand, and expand on each other's work, driving efficiency and fostering innovation.
Is a graph database compatible with existing lab software?
Yes, Labbit's modern graph databases offer integration options such as APIs and connectors, allowing them to work alongside existing lab software. This compatibility ensures labs can enhance their data capabilities without overhauling their current systems.
Why Graph Databases Excel
Although relational databases have been the default for lab software for many years, advances in technology and data modeling mean that labs can now choose a more flexible, proven option—the non-relational RDF graph database. Modern RDF graph databases are at the forefront of data storage. Because they can help future-proof software, they have been widely adopted by cutting-edge organizations with a heavy research focus.
If your lab is facing yet another replatforming or database migration, we recommend considering using an RDF graph database instead, as part of your overall laboratory management data solution. Or, you might choose a LIMS like Labbit which natively employs an RDF graph database.
Learn More About Graph Databases For Lab Software
Labbit removes laboratory configuration bottlenecks, enabling you to simplify workflows, collaborate seamlessly, and empower new discoveries on a scalable and future-proof platform. Contact us for a free consultation.
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