Unlocking business insights with graph databases

Graph databases facilitate the curation of views over multiple data sources, marking up the connections between business data silos as metadata. Rather than aiming for a monolithic master database, the focus is on integrating data silos through APIs, query interfaces, and interactive visualisations. This aligns with modern data platform architectures, emphasising decentralisation, microservices, and agility through DevOps and DataOps methodologies.

Within this article, we explore the key considerations to ensure a successful graph database implementation in your organisation.  This includes data modelling, building pipelines, scalability, security, and data lineage.

Choosing the right graph database model

When considering graph solutions, two well-supported graph database operating models emerge:

• Labelled Property Graphs (LPGs): A data model used in graph databases that represents information as nodes, relationships, and associated properties, with the ability to label nodes and relationships for better organisation
  
• Resource Description Framework (RDF) Graphs: A standard model for representing data using subject-predicate-object triples. RDF graphs promote the use of globally referenceable ontologies, facilitating seamless integration of data from multiple sources

It is important to carefully consider which approach is best suited to your use case. The standardisation offered by an RDF supports a high degree of compatibility which may prove to be an important feature in situations in scenarios involving the need to share graphs across multiple entities. However, interoperability does come at the cost of flexibility. If maintaining flexibility is a critical requirement, an LPG may offer a more appropriate solution.  

For more information on selecting the best model for your situation, please refer to our previous article: Unleashing the power of graph databases to discover hidden data connections 

Building data pipelines

Building data pipelines to feed a graph database is a crucial process that ensures a steady and reliable flow of data into the system. The pipeline design begins with understanding the data sources and formats. These could include relational databases, log files, APIs, or streaming data. Extracting data from these sources requires robust data extraction techniques and then the extracted data needs to be transformed and cleaned to match the graph database’s schema and structure. This transformation may involve data enrichment, normalisation, and validation to ensure data consistency and accuracy.

Once the data is prepared, it can be loaded into the graph database. Regular monitoring and error handling are essential to identify and address any issues that may arise during the data pipeline’s operation. By implementing well-designed data pipelines, organisations can ensure that their graph database remains up-to-date with the latest information, enabling powerful insights and efficient analysis of interconnected data.

Tracking data lineage

When building your data pipelines, think about incorporating features to track data lineage. Data lineage refers to the ability to trace and document the origin, movement, and transformation of data throughout its lifecycle. By establishing data lineage, organisations can gain insights into how data flows through different stages of the pipeline and ensure data quality, compliance, and governance.

To track data lineage, metadata must be collected and recorded at each step of the data pipeline. This metadata includes information about the data’s source, transformation processes, and the destination in the graph database. Data lineage tools and platforms can automate this process, capturing details such as data origins, timestamps, and transformation rules.

By incorporating robust data lineage tracking into the data pipeline, organisations can safeguard data quality, maintain data integrity, and gain a comprehensive understanding of the data’s journey, enhancing the reliability and trustworthiness of the graph database and the insights derived from it.

Ensuring scalability in graph databases

When using RDF graphs, the data model is declared using the Web Ontology Language (OWL), where data classes and properties are identified with URIs. In a LPG, there is no formal specification for the taxonomy/schema of the data, and typically short strings are used instead. The important difference is that references to OWL ontologies are globally referenceable and therefore shared among data publishers. For example, the Financial Industry Business Ontology (FIBO) is used by publishers in the finance industry such as Bloomberg and Thomson Reuters.

When data from each publisher lists an entity as being a “FIBO Student Loan” (specifically, by using the following URL) there is a shared and documented understanding of what that means (for example, a student loan in this case). It also means that a graph loaded with data from Bloomberg and Thomson Reuters can be queried for a FIBO Student Loan, and data from both publishers will be returned and considered in the query. Thus, data from multiple sources is seamlessly integrated into a single graph.

Security Considerations

For most situations, data security is paramount. When selecting and implementing a graph database, you should give careful consideration to the configuration of role-based access control (RBAC) and fine-grained permissions. It’s important to ensure that encryption is employed to protect data, both at rest, and in transit. This will prevent unauthorised access, even if the database or network is compromised. It is also important to put in place auditing mechanisms to track and monitor user activities. This will enable swift detection of potential security breaches.

Once implemented, you should undertake regular security assessments and consider undertaking penetration testing to identify vulnerabilities or areas for improvement. By taking a proactive approach, and adhering to best security practices, you can build a robust and secure graph database environment which ensures your data’s confidentiality, integrity, and availability.

Key tips for successful graph database implementation

• Know your use cases: Evaluate your graph database choice based on your data model size and anticipated future usage
• Design for scale-up: Ensure your data pipelines are capable of handling large data streams to support future expansions
• Formalise the data model: Embrace expressive data models early-on to maximise the benefits of the graph process
• Record data provenance: Maintain a complete record of data lineage to comply with data regulations and ensure data integrity
• Investigate RBAC within the graph: Explore RBAC using ontologies within the graph to enhance data security

Conclusion

In the rapidly evolving landscape of data-driven businesses, graph databases present an agile and powerful solution to harness the full potential of organisational data. By enabling considered views, uncovering latent connections and supporting scalable implementations, graph databases open up new possibilities for insightful decision-making and innovative problem-solving. At 6point6, we possess extensive experience in delivering successful graph database solutions using our DataOps methodology, helping businesses thrive in the data-centric era.

For more information please contact us.