#graphdatabases

Spanner Graph

Operational databases offer the basis for creating enterprise AI applications that are precise, pertinent, and based on enterprise truth as the use of  AI continues to grow. At Google Cloud, the team strive to provide the greatest databases for creating and executing AI applications. In light of this, they are thrilled to introduce Graph Spanner, vector search, and sophisticated full-text search a few new features that will make it simpler for you to create effective gen AI products.

With the release of Bigtable SQL and Bigtable distributed counters, the team also revolutionising the developer experience by simplifying the process of creating at-scale apps. Lastly, they are announcing significant updates to help customers modernise their data estates by supporting their traditional corporate workloads, such as SQL Server and Oracle. Now let’s get started!

Using a graph database can be a valuable, albeit complex, approach for enterprises to gain insights from their connected data so they can create more intelligent applications. Google is excited to present Spanner Graph today, a ground-breaking solution that combines Spanner, Google’s constantly available, globally consistent database, with capabilities specifically designed for graph databases.

Graphs are a natural way to show relationships in data, which makes them useful for analysing data that is related, finding hidden patterns, and enabling applications that depend on connection knowledge. There are several applications for graphs, including route planning, customer 360, fraud detection, recommendation engines, network security, knowledge graphs, and data lineage tracing.

But implementing separate graph databases to handle these use cases frequently comes with the following drawbacks:

Data fragmentation and operational overhead: Separate graph database maintenance frequently results in data silos, added complexity, and inconsistent data copies, all of which make it more difficult to conduct effective analysis and decision-making.

Bottlenecks in terms of scalability and availability: As data volumes and complexity increase and impede corporate growth, many standalone graph databases find it difficult to fulfil the expectations of mission-critical applications in terms of scalability and availability.

Ecosystem friction and skill gaps: Adopting a fully new graph paradigm may be more difficult for organisations because of their significant investments in infrastructure and SQL knowledge. They require more resources and training to accomplish this, which could take resources away from other pressing corporate requirements.

With almost infinite scalability, Graph Spanner reinvents graph data management by delivering a unified database that seamlessly merges relational, search, graph, and  AI capabilities. Spanner Graph provides you with:

Native graph experience: Based on open standards, the ISO Graph Query Language (GQL) interface provides a simple and clear method for matching patterns and navigating relationships.

Unified relational and graph models: Complete GQL and SQL interoperability eliminates data silos and gives developers the freedom to select the best tool for each given query. Data from tables and graphs can be tightly integrated to reduce operational overhead and the requirement for expensive, time-consuming data transfers.

Built-in search features: Graph data may be efficiently retrieved using keywords and semantic meaning thanks to rich vector and full-text search features.

Scalability, availability, and consistency that lead the industry: You can rely on Spanner’s renowned scalability, availability, and consistency to deliver reliable data foundations.

AI-powered insights: By integrating deeply with Vertex AI, Spanner Graph gains direct access to a robust set of AI models, which speeds up AI workflows.

Let’s examine more closely what makes Spanner Graph special

Spanner Graph provides a recognisable and adaptable graph database interface. Supported by Graph Spanner is ISO GQL, the latest global standard for graph databases. It makes it simpler to find hidden links and insights by providing a clear and simple method for matching patterns, navigating relationships, and filtering results in graph data.

Spanner Graph functions well with both full-text and vector searches. With the combination, you may use GQL to navigate relationships within graph structures and search to locate graph contents. To be more precise, you can use full-text search to identify nodes or edges that include particular keywords or use vector search to find nodes or edges based on semantic meaning. GQL allows you to easily explore the remainder of the graph from these beginning locations. This unified capability enables you to find hidden connections, patterns, and insights that would be challenging to find using any one method by combining various complimentary strategies.

Spanner Graph provides industry-leading consistency and availability while scaling beyond trillions of edges. Since Graph Spanner inherits Spanner’s nearly limitless scalability, industry-best availability, and worldwide consistency, it’s an excellent choice for even the most crucial graph applications. Specifically, without requiring your involvement, Spanner’s transparent sharding may leverage massively parallel query processing and scale elastically to very huge data sets.

Spanner Graph’s tight integration with Vertex  AI speeds up your AI workflows. Spanner Graph has a close integration with Vertex AI, the unified, fully managed AI development platform offered by Google Cloud. Using the Graph Spanner schema and query, you may immediately access Vertex AI’s vast collection of generative and predictive models, which can expedite your AI workflow. To enrich your graph, for instance, you can utilise LLMs to build text embeddings for graph nodes and edges. After that, you can use vector search to extract data from your graph in the semantic space.

You may create more intelligent applications with Spanner Graph

With practically limitless scalability, Spanner Graph effortlessly combines graph, relational, search, and AI capabilities, creating a plethora of opportunities:

Product recommendations: To create a knowledge network full of context, Spanner network represents the intricate interactions that exist between people, products, and preferences. By fusing full-text search with quick graph traversal, you may recommend products based on user searches, past purchases, preferences, and similarity to other users.

Financial fraud detection: It is simpler to spot questionable connections when financial entities like accounts, transactions, and people are represented naturally in Spanner Graphs. Similar patterns and anomalies in the embedding space are found by vector search. Financial institutions can minimise financial losses by promptly and correctly identifying possible dangers through the combination of these technologies.

Social networks: Even in the biggest social networks, the Graph Spanner model logically individuals, groups, interests, and interactions. For individualised recommendations, it facilitates the quick identification of trends like overlapping group memberships, mutual friends, or related interests. Users can quickly locate individuals, groups, posts, or particular subjects by using natural language searches with the integrated full-text search feature.

Gaming: Player characters, objects, places, and the connections between them are all natural representations of elements in game environments. Effective link traversal is made possible by the Spanner Graph, and this is crucial for game features like pathfinding, inventory control, and social interactions. Furthermore, even at times of high usage, Spanner Graph’s global consistency and scalability ensure a smooth and fair experience for every player.

Network security: Recognising trends and abnormalities requires an understanding of the relationships that exist between individuals, devices, and events over time. Security experts can employ graph capabilities to trace the origins of attacks, evaluate the effect of security breaches, and correlate these findings with temporal trends for proactive threat detection and mitigation thanks to Graph Spanner relational and graph interoperability.

GraphRAG: By utilising a knowledge graph to anchor foundation models, Spanner Graph elevates Retrieval Augmented Generation (RAG) to a new level. Furthermore, Spanner Graph’s integration of tabular and graph data enhances your AI applications by providing contextual information that neither format could provide on its own. It is capable of handling even the largest knowledge graphs due to its unparalleled scalability. Your GenAI workflows are streamlined with integrated Vertex  AI and built-in vector search.