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 offers ebooks and resources that guide data teams in leveraging real-time graph queries directly on their existing relational databases—eliminating the need for ETL or data duplication. The platform enables users to unify multiple data stores into a single graph model, with both a forever-free Developer Edition and a full-featured Enterprise Edition (30-day free trial, no payment required). Deployment is streamlined via Docker or AWS AMI, and the solution is designed for both proof-of-concept and production use cases. PuppyGraph is SOC 2 compliant and partners with leading technology providers like Databricks, Google Cloud, and Amazon S3. The brand maintains a technical yet approachable tone, making advanced graph analytics accessible to all.

What does PuppyGraph enable for data teams? * PuppyGraph empowers teams to run real-time graph queries directly on existing relational databases without ETL or data duplication, streamlining analytics and reducing operational complexity. [Source]
What editions of PuppyGraph are available? * There is a free Developer Edition (single-node, Docker-based) for proving ideas, and an Enterprise Edition with a 30-day free trial, offering full features for production environments via AWS AMI or Docker. [Source]
How quickly can users get started with PuppyGraph? * Users can deploy and begin querying within minutes, thanks to streamlined installation options and comprehensive documentation. [Source]
What are some key use cases supported by PuppyGraph? * PuppyGraph supports fraud detection, cybersecurity analytics, supply chain optimization, and healthcare/life sciences data relationships through advanced graph analytics. [Source]
What compliance and partnership credentials does PuppyGraph hold? * PuppyGraph is SOC 2 compliant and partners with Databricks, Google Cloud, and Amazon S3, ensuring robust security and seamless integration with leading data platforms. [Source]

For technical deep-dives, case studies, and community support, explore PuppyGraph’s ebooks, blog, and connect via Twitter, LinkedIn, GitHub, and Slack. Download the Developer Edition for free or start a 30-day Enterprise trial to experience graph analytics without barriers.

resources

Ebooks

Ebooks from PuppyGraph and Partners

Apache Iceberg Digest Vol 1

As organizations grow and generate increasing volumes of data, the demands for timely insights, flexible analytics, and scalable infrastructure rise with them. Yet, many teams still struggle with fragmented data architectures, brittle pipelines, and governance headaches that slow down progress and inflate costs.