Source URL: https://cloud.google.com/blog/products/databases/spanner-graph-is-now-ga/
Source: Cloud Blog
Title: Announcing the general availability of Spanner Graph
Feedly Summary: In today’s complex digital world, building truly intelligent applications requires more than just raw data — you need to understand the intricate relationships within that data. Graph analysis helps reveal these hidden connections, and when combined with techniques like full-text search and vector search, enables you to deliver a new class of AI-enabled application experiences. However, traditional approaches based on niche tools result in data silos, operational overhead, and scalability challenges. That’s why we introduced Spanner Graph, and today we’re excited to announce that it’s generally available.
In a previous post, we described how Spanner Graph reimagines graph data management with a unified database that integrates graph, relational, search, and gen AI capabilities with virtually unlimited scalability. With Spanner Graph, you gain access to an intuitive ISO Standard Graph Query Language (GQL) interface that simplifies pattern matching and relationship traversal. You also benefit from full interoperability between GQL and SQL, for tight integration between graph and tabular data. Powerful vector and full-text search enable fast data retrieval using semantic meaning and keywords. And you can rely on Spanner’s scalability, availability, and consistency to provide a solid data foundation. Finally, integration with Vertex AI gives you access to powerful AI models directly within Spanner Graph.
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What’s new in Spanner Graph
Since the preview, we have added exciting new capabilities and partner integrations to make it easier for you to build with Spanner Graph. Let’s take a closer look.
1) Spanner Graph Notebook: Graph visualization is key to developing with graphs. The new open-source Spanner Graph Notebook tool provides an efficient way to query Spanner Graph visually. This tool is natively installed in Google Colab, meaning you can use it directly within that environment. You can also leverage it in notebook environments like Jupyter Notebook. With this tool, you can use magic commands with GQL to visualize query results and graph schemas with multiple layout options, inspect node and edge properties, and analyze neighbor relationships.
Open-source Spanner Graph Notebook.
2) GraphRAG with LangChain integration: Spanner Graph’s integration with LangChain allows for quick prototyping of GraphRAG applications. Conventional RAG, while capable of grounding the LLM by providing relevant context from your data using vector search, cannot leverage the implicit relationships present in your data. GraphRAG overcomes this limitation by constructing a graph from your data that captures these complex relationships. At retrieval time, GraphRAG uses the combined power of graph queries with vector search to provide a richer context to the LLM, enabling it to generate more accurate and relevant answers.
3) Graph schema in Spanner Studio: The Spanner Studio Explorer panel now displays a list of defined graphs, their nodes and edges, and the associated labels and properties. You can explore and understand the structure of your graph data at a glance, making it easier to design, debug, and optimize your applications.
4) Graph query improvements: Spanner Graph now supports the path data type and functions, allowing you to retrieve and analyze the specific sequence of nodes and relationships that connect two nodes in your graph. For example, you can create a path variable in a path pattern, using the IS_ACYCLIC function to check if the path has repeating nodes, and return the entire path:
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<ListValue: [StructValue([(‘code’, ‘GRAPH FinGraph\r\nMATCH p = (:Account)-[:Transfers]->{2,5}(:Account)\r\nRETURN IS_ACYCLIC(p) AS is_acyclic_path, TO_JSON(p) AS full_path;’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ebe12caf910>)])]>
5) Graph visualization partner integrations: Spanner Graph is now integrated with leading graph visualization partners. For example, Spanner Graph customers can use GraphXR, Kineviz’s flagship product, which combines cutting-edge visualization technology with advanced analytics to help organizations make sense of complex, connected data.
“We are thrilled to partner with Google Cloud to bring graph analytics to big data. By integrating GraphXR with Spanner Graph, we’re empowering businesses to visualize and interact with their data in ways that were previously unimaginable." – Weidong Yang, CEO, Kineviz
Transit Fraud Investigation in Kineviz GraphXR and Google Spanner Graph.
Similarly, you can use Graphistry’s GPU-accelerated visual graph intelligence platform to extract meaningful insights from large complex data in Spanner Graph.
"Businesses can finally handle graph data with both speed and scale. By combining Graphistry’s GPU-accelerated graph visualization and AI with Spanner Graph’s global-scale querying, teams can now easily go all the way from raw data to graph-informed action. Whether detecting fraud, analyzing journeys, hunting hackers, or surfacing risks, this partnership is enabling teams to move with confidence." – Leo Meyerovich, Founder and CEO, Graphistry
Visual analytics capabilities in Graphistry: zooming, clustering, filtering, histograms, time bar filtering, node styling (colors), allowing point-and-click analysis to quickly understand the data, clusters, identify patterns, anomalies and other insights.
Furthermore, you can use G.V(), a quick-to-install graph database client, with Spanner Graph to perform day-to-day graph visualization and data analytics tasks with ease. Data professionals benefit from high-performance graph visualization, no-code data exploration, and highly customizable data visualization options.
“Graphs thrive on connections, which is why I’m so excited about this new partnership between G.V() and Google Cloud Spanner Graph. Spanner Graph turns big data into graphs, and G.V() effortlessly turns graphs into interactive data visualizations. I’m keen to see what data professionals build combining both solutions.” – Arthur Bigeard, Founder, gdotv Ltd.
Visually querying and exploring Spanner Graph with G.V().
What customers are saying
Through our road to GA, we have also been working with multiple customers to help them innovate with Spanner Graph:
“The Commercial Financial Network manages commercial credit data for more than 30 million U.S. businesses Managing the hierarchy of these businesses can be complex due to the volume of these hierarchies, as well as the dynamic nature driven by mergers and acquisitions, Equifax is committed to providing lenders with the accurate, reliable and timely information they need as they make financial decisions. Spanner Graph helps us manage these rapidly changing, dynamic business hierarchies easily at scale.” – Yuvaraj Sankaran, Chief Architect of Global Platforms, Equifax
“As we strive to enhance our fraud detection capabilities, having a robust, multi-model database like Google Spanner is crucial for our success. By integrating SQL for transactional data management with advanced graph data analysis, we can efficiently manage and analyze evaluated fraud data. Spanner’s new capabilities significantly improve our ability to maintain data integrity and uncover complex fraud patterns, ensuring our systems are secure and reliable.” – Hai Sadon, Data Platform Group Manager, Transmit Security
"Spanner Graph has provided a novel and performant way for us to query this data, allowing us to deliver features faster and with greater peace of mind. Its flexible data modeling and high-performance querying have made it far easier to leverage the vast amount of data we have in our online applications." – Aaron Tang, Senior Principal Engineer, U-NEXT
Get started with Spanner Graph today
With Spanner Graph, we’re excited to offer graph data management alongside relational, search, and AI capabilities, all on a single unified, highly scalable database. Learn more about Spanner Graph benefits and use cases here. Use this quick setup guide to get started with Spanner Graph capabilities. You can also try out sample applications related to financial investing, fraud detection, and customer 360 and product recommendations.
AI Summary and Description: Yes
Summary: The text discusses the release of Spanner Graph, emphasizing its capabilities in synthetic data management by integrating graph data with traditional relational databases along with AI features. This development is significant for professionals in AI and cloud computing as it offers novel ways to manage complex data relationships and enhance AI applications through innovative querying techniques.
Detailed Description:
– **Introduction of Spanner Graph:**
– Spanner Graph merges graph, relational, search, and generative AI capabilities into a unified database, overcoming traditional data silos and scalability issues.
– Offers an intuitive Graph Query Language (GQL) for efficient querying and relationship traversal.
– **Key Features:**
– **Spanner Graph Notebook:**
– An open-source tool for graphical data visualization.
– Integration with environments like Google Colab and Jupyter Notebook to query and analyze graph data visually.
– **GraphRAG with LangChain Integration:**
– Enhances traditional Retrieval-Augmented Generation (RAG) by leveraging relationships in data for improved accuracy in LLM outputs.
– **Graph Schema Visualization:**
– Provides users with an overview of graph data structures, promoting easier design and debugging.
– **Path Query Improvements:**
– New functionalities for analyzing node relationships using path data types in queries.
– **Partner Integrations for Visualization:**
– Collaborations with companies like GraphXR and Graphistry to enhance the visualization of graph data in Spanner Graph.
– **Feedback from Early Users:**
– Customers express satisfaction with the ability to manage complex hierarchies and improve fraud detection capabilities thanks to Spanner Graph’s advanced analytics.
– Provides a framework for quickly delivering features while ensuring data integrity.
– **Practical Implications:**
– This advancement in graph data management is pivotal for industries relying on complex data analytics, such as finance and security.
– The integration of advanced search and generative AI capabilities can significantly influence how businesses manage and derive insights from their data.
Through these advancements, Spanner Graph aligns with the evolving needs of data professionals seeking seamless integration of AI within graph-based applications while boosting operational efficiency and reducing overhead.