VENDORiQ: Google Brings Graph Processing to Spanner Database

Why It’s Important

The addition of graph processing capabilities natively inside their flagship database is further evidence that Google continues to invest in AI for enterprise applications and other hyperscale cloud vendors are expected to follow with similar offerings. Graph-enabled AI applications are going to be developed sooner than expected. Enterprises will experiment with this in their retrieval augmented generation (RAG) pipelines for information retrieval, recommendation, search, pattern detection, classification and natural language processing applications.

This development reflects the ongoing trend of integrating AI capabilities into core database services. While potentially beneficial, this convergence necessitates careful evaluation of its practical value and implementation challenges. Microsoft recently released open-source code for its graph-based RAG system. This is aligned with our prediction of a broader industry movement towards integrating graph capabilities with AI technologies, which will lead to further developments in graph database services over the coming years.

IBRS had earlier explained that mapping and including graph properties may offer improved context for data relationships. However, the actual impact on reducing errors in large language model (LLM) applications remains to be empirically validated in real-world scenarios. Moreover, graph-based queries often introduce additional computational complexity. Organisations must carefully assess the performance implications, particularly in terms of latency and resource requirements, against the potential benefits.

Spanner Graph represents another step in the ongoing evolution of database technologies. While the convergence of relational, NoSQL, and graph capabilities offers potential versatility, it also increases system complexity and may require significant adjustments to existing data architectures.

Who’s Impacted

• CIO/CTO
• Data architects
• AI/ML development teams
• Enterprise application managers
• Data governance teams

What’s Next?

• Identify specific use cases where graph databases could add value, but maintain a critical perspective on their necessity and ROI. 
• Consider small-scale pilot projects to empirically test the viability of graph-enabled applications in your business context before any large-scale adoption.
• Assess the current skill gap in your organisation regarding graph database concepts and graph-based AI architectures. Consider the total cost of ownership, including potential training of IT development teams or hiring needs, when evaluating graph database adoption.
• Benchmark any graph-enabled applications against current solutions, focusing on tangible metrics such as performance, accuracy, and business value.
• Approach any integration of graph databases as a significant architectural decision, not a minor addition. Engage data architects to critically review current data models and assess the challenges of graph-based approaches.
• Develop comprehensive policies to address potential privacy and security concerns inherent in interconnected graph data. Implement robust access controls and audit mechanisms for graph data structures.
• Rigorously test graph-enabled applications for performance impacts, particularly in terms of query latency and resource utilisation. Develop scalability plans that account for the potentially increased computational demands of graph processing.

The introduction of Spanner Graph by Google represents an interesting development in database technology, offering new capabilities in data management and AI application development. However, senior IT leaders must approach this development with a critical and measured perspective. Organisations should carefully evaluate the true value proposition of graph-enabled databases against their specific needs, existing infrastructure, and long-term strategic goals. While graph databases may offer benefits in certain scenarios, they also introduce new complexities and challenges that must be thoroughly considered.

As the data landscape continues to evolve, the key to success lies not in rapidly adopting every new technology, but in thoughtfully assessing how emerging capabilities align with and enhance your organisation’s unique data strategy and business objectives.

Related IBRS Advisory

1. VENDORiQ: Graph-Based Retrieval-Augmented Generation: Business Impact of GraphRAG Release – IBRS
2. VENDORiQ: Database Managers – Get Ready, This is Going to be a Wild Ride: Google and Microsoft Merge LLMs Into Mainstream Databases – IBRS