PuppyGraph Unveils Native Integration with Databricks’ Managed Iceberg Tables, Enabling Real-Time Graph Analytics
PuppyGraph, the pioneering real-time, zero-ETL graph query engine, has announced a groundbreaking native integration with Managed Iceberg Tables on the Databricks Data Intelligence Platform. This milestone empowers organizations to execute complex graph queries directly on Iceberg Tables governed by Unity Catalog—without the need for data movement or ETL pipelines. The announcement coincides with the public preview launch of Databricks Managed Iceberg Tables at this year’s Data + AI Summit, further solidifying the synergy between PuppyGraph and Databricks in driving innovation in the modern data stack.
Revolutionizing Graph Analytics with Zero-ETL Efficiency
The integration of PuppyGraph with Managed Iceberg Tables eliminates traditional barriers to graph analytics, such as data silos and time-consuming ETL processes. By leveraging PuppyGraph’s in-place graph engine, teams can now perform real-time graph queries on massive datasets stored in Iceberg Tables. This seamless capability is made possible by Databricks’ full support for the Apache Iceberg™ REST Catalog API, which enables external engines like Apache Spark™, Apache Flink™, and Apache Kafka™ to interoperate effortlessly with tables governed by Unity Catalog.
Managed Iceberg Tables bring additional advantages, including automatic performance optimizations that deliver cost-efficient storage and lightning-fast query speeds right out of the box. Combined with PuppyGraph’s real-time graph querying capabilities, this integration transforms how organizations analyze relationships within their data, enabling deeper insights and faster decision-making.
Unlocking New Possibilities for Data Teams
The collaboration between PuppyGraph and Databricks opens up a world of possibilities for data teams across industries. With this integration, organizations can now:
- Query Massive Iceberg Datasets as Live Graphs: Analyze large-scale datasets in real-time, treating them as dynamic graphs rather than static tables.
- Detect Complex Patterns with Graph Traversal: Use graph traversal techniques to uncover fraud, lateral movement in cybersecurity, and intricate network paths, all without moving data or building separate pipelines.
- Perform Root Cause Analysis on Telemetry Data: Build service relationship graphs to identify the root causes of issues in complex systems, improving operational efficiency and reducing downtime.
- Eliminate Siloed Graph Databases: Remove the need to extract, transform, and load (ETL) data into standalone graph databases, streamlining workflows and reducing infrastructure costs.
- Scale Analytics Across Petabytes: Achieve unparalleled scalability with minimal operational overhead, allowing teams to handle petabyte-scale datasets efficiently.
Real-World Impact: Insights from Joint Customers
Joint customers like Coinbase and CipherOwl have already harnessed the power of this integration to drive their products and gain real-time insights directly from their managed lakehouses. At the Data + AI Summit, both companies will share case studies demonstrating how graph analytics has revolutionized their operations.
For example, Coinbase leveraged PuppyGraph’s real-time graph queries to enhance fraud detection and improve customer trust by identifying suspicious patterns in transaction data. Similarly, CipherOwl utilized the integration to streamline its cybersecurity efforts, detecting lateral movement and potential threats within its network infrastructure more effectively.
A Paradigm Shift in the Modern Data Stack
“This changes how graph analytics fits into the modern data stack,” said Weimo Liu, CEO of PuppyGraph. “Databricks’ new Iceberg capabilities provide a truly open, scalable foundation. With PuppyGraph, teams can ask complex relationship-driven questions without ever leaving their lakehouse.”
The integration represents a paradigm shift in how organizations approach graph analytics. Traditionally, performing graph queries required extracting data from data lakes or warehouses and loading it into specialized graph databases—a process that introduced latency and complexity. With PuppyGraph’s zero-ETL approach, these challenges are eliminated, allowing teams to derive insights directly from their existing data infrastructure.
Why This Matters for the Future of Data Analytics
The combination of PuppyGraph and Databricks’ Managed Iceberg Tables addresses two critical needs in the modern data landscape: scalability and real-time insights. As organizations grapple with ever-growing datasets and increasingly complex queries, solutions that enable efficient, real-time analysis are becoming indispensable.
By removing the need for ETL pipelines and enabling direct graph queries on Iceberg Tables, PuppyGraph reduces operational overhead while enhancing performance. This allows businesses to focus on deriving value from their data rather than managing cumbersome infrastructure. Moreover, the openness of the Apache Iceberg™ ecosystem ensures compatibility with a wide range of tools and frameworks, future-proofing investments in data analytics.
How to Get Started
To explore how PuppyGraph integrates with Apache Iceberg™ and the Databricks Data Intelligence Platform, visit www.puppygraph.com/databricks. Additionally, attendees of the Data + AI Summit 2025 can catch a joint talk featuring Coinbase, where they will share practical insights and success stories from their experience using PuppyGraph and Databricks together.
Empowering Organizations with Real-Time Graph Analytics
In an era where data is king, the ability to analyze relationships and patterns in real-time is a game-changer. PuppyGraph’s native integration with Databricks’ Managed Iceberg Tables redefines what’s possible in graph analytics, offering organizations a powerful, scalable, and cost-effective solution. By combining the openness and scale of Iceberg Tables with PuppyGraph’s real-time querying capabilities, businesses can unlock deeper insights, accelerate innovation, and stay ahead in an increasingly competitive landscape.
As the lines between data lakes, warehouses, and graph databases continue to blur, PuppyGraph and Databricks are leading the charge in creating a unified, intelligent data ecosystem. This partnership not only simplifies the technical complexities of graph analytics but also democratizes access to advanced insights, empowering teams to achieve more with less effort.
About PuppyGraph:
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 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, Netskope, CipherOwl, Prevalent AI, Clarivate, and more.
