puppygraph-professional

PuppyGraph is a real-time, zero-ETL graph query engine designed for teams that need to analyze complex relationships in large-scale data without introducing a separate graph database or building ETL pipelines. Instead of copying data into a native graph store, PuppyGraph queries existing data lakes and relational databases directly and presents them as a unified graph model. This allows users to run deep, multi-hop graph queries while keeping data in place, governed, and accessible by existing analytics tools. PuppyGraph is built for data engineers, platform teams, analytics engineers, and architects who need to support use cases such as cybersecurity investigations (SIEM graph, cloud security analysis, attack path), observability, dependency analysis, fraud detection, knowledge graphs for AI systems, and relationship-heavy analytics. It is especially well suited for teams operating at scale, where data volumes, schema evolution, and operational complexity make traditional graph databases difficult to adopt and maintain. Many organizations recognize the value of graph analytics but struggle with the cost and complexity of native graph databases. Common challenges include maintaining fragile ETL pipelines, duplicating large datasets, slow query performance at scale, and the operational burden of managing an additional storage system. PuppyGraph addresses these problems by separating graph computation from storage and running graph queries directly on existing data platforms. By eliminating ETL, reducing infrastructure overhead, and enabling fast multi-hop queries on petabyte-scale data, PuppyGraph makes graph analytics practical for modern data and AI workloads.