Neo4j Graph Data Science is integrated with Neo4j’s native graph database, but implementing Graph Data Science is quite different from running a transactional or operational Neo4j database.
This guide explains how to align hardware resources and architectural design for optimal performance of graph data science workloads. You’ll learn about:
- Scaling for large workloads and multiple concurrent users
- The impact of memory and CPU on data science performance
- How graph algorithms execute and tuning parameters to consider
Fill out the form to get your copy of the Neo4j Graph Data Science Configuration Guide.