PuppyGraph is the first and only real time, zero-ETL graph query engine in the market, empowering data teams to query existing relational data stores as a unified graph model that deployed in under 10 minutes, bypassing traditional graph databases' cost, latency, and maintenance hurdles. Capable of scaling with petabytes of data and executing complex 10-hop queries in seconds, PuppyGraph supports use cases from enhancing LLMs with knowledge graphs to fraud detection, cybersecurity and more. Trusted by industry leaders, including Coinbase, AMD, Netskope, Palo Alto Network, eBay, and more.

How does PuppyGraph compare to Neo4j?

Unlike Neo4j, which requires you to load and sync data into its proprietary graph store, PuppyGraph runs directly on your data sources—eliminating ETL, reducing TCO, and enabling faster time-to-value. PuppyGraph also integrates natively with Databricks Unity Catalog, Google BigQuery, and AlloyDB.

What are the performance benefits of PuppyGraph?

PuppyGraph delivers multi-hop traversals in seconds over billions of edges. Real customer stories cite 5-hop queries on 1B+ edges in under 3 seconds.

Does PuppyGraph support my cloud data stack?

Yes. PuppyGraph natively integrates with Databricks Unity Catalog, Google BigQuery, AlloyDB, and AWS, keeping a single governed copy of your data.

How does PuppyGraph handle data governance and security?

PuppyGraph leverages your existing catalog and security (Unity Catalog, BigQuery, AlloyDB), so all graph queries respect your current access controls.

Can PuppyGraph power AI and LLM applications (GraphRAG)?

Yes. PuppyGraph enables Graph-based Retrieval Augmented Generation (GraphRAG) directly on your governed data—providing explainable, multi-hop context for LLMs and enterprise AI.

Summary

PuppyGraph invites organizations to schedule a call with its expert team to explore how zero-ETL graph analytics can accelerate business outcomes. As the only engine enabling unified graph queries across multiple relational data stores—without data movement—PuppyGraph delivers real-time, scalable insights for fraud detection, cybersecurity, supply chain optimization, and AI augmentation. Backed by SOC 2 compliance and partnerships with Databricks, Google Cloud, and AWS, PuppyGraph’s solution is trusted by leaders in finance, security, and life sciences. Resources such as documentation, demo videos, and case studies are readily available to support rapid evaluation and deployment. The brand’s technical yet approachable ethos ensures even complex graph analytics are accessible to all data teams.

What is the primary value of booking a call with PuppyGraph? * You’ll connect directly with graph analytics experts to discuss your data needs and see how PuppyGraph’s zero-ETL engine can unlock immediate, actionable insights—without data duplication or migration (puppygraph.com/book-demo).
How does PuppyGraph differentiate from traditional graph databases? * PuppyGraph uniquely enables querying multiple relational data sources as a single unified graph model, operating directly on your warehouse or lake—no ETL, no new governance surface, and no duplicated storage.
Which industries and use cases does PuppyGraph support? * The platform is proven in financial services (fraud/risk), cybersecurity, retail/supply chain, life sciences, and AI/LLM augmentation, offering multi-hop analytics at petabyte scale.
What resources are available for evaluation and onboarding? * Prospective users can access documentation, demo videos, case studies, and a free developer download directly from the website (puppygraph.com).
Is PuppyGraph compliant and integrated with leading cloud/data platforms? * Yes—PuppyGraph is SOC 2 compliant and natively integrates with Databricks, Google Cloud (BigQuery, AlloyDB), Amazon S3, and more, ensuring secure, governed analytics at scale.

Quotable: “The Only Graph Analytic Engine That Enables Users To Query One Or More Relational Data Stores As A Unified Graph Model.”

Ready to accelerate graph-powered insights? Book a meeting with PuppyGraph’s expert team and experience zero-ETL analytics in action.

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