Graph Database Migration Guide
Migrate Neo4j, Amazon Neptune, Azure Cosmos DB with 100% relationship preservation, 10x faster queries, and zero downtime in 2-3 weeks
Complete Graph Migration Scope
Graph Data Objects
- Nodes with labels and properties
- Relationships with types and properties
- Property graphs and RDF triples
- Graph indexes and constraints
Query Migration
- Cypher to Gremlin conversion
- SPARQL query translation
- Graph algorithms migration
- Stored procedures conversion
Application Integration
- Driver and SDK updates
- API endpoint migration
- Connection pooling configuration
- Authentication and authorization
Performance Optimization
- Index strategy optimization
- Query performance tuning
- Partitioning and sharding
- Caching layer implementation
4-Phase Graph Migration Process
Phase 1: Graph Analysis (Days 1-3)
- • AI analyzes graph schema, node labels, and relationship types
- • Identifies property patterns and data types
- • Analyzes query patterns and graph algorithms
- • Assesses index usage and performance characteristics
- • Creates comprehensive migration plan
Phase 2: Schema Mapping (Days 4-7)
- • AI maps node labels and relationship types
- • Converts property definitions and constraints
- • Translates Cypher to Gremlin or SPARQL
- • Optimizes index strategy for target platform
- • Validates schema compatibility
Phase 3: Data Migration (Days 8-14)
- • Parallel node and relationship migration
- • 100% relationship preservation with validation
- • Property data type conversion
- • Continuous validation and reconciliation
- • Zero downtime with dual-write pattern
Phase 4: Cutover & Optimization (Days 15-21)
- • Application driver and SDK updates
- • Query performance testing and optimization
- • Graph algorithm validation
- • Final reconciliation and cutover
- • Performance monitoring and tuning
Graph Database Platform Comparison
| Feature | Neo4j | Amazon Neptune | Azure Cosmos DB |
|---|---|---|---|
| Query Language | Cypher | Gremlin, SPARQL | Gremlin |
| Graph Model | Property Graph | Property Graph, RDF | Property Graph |
| Deployment | Self-hosted, Aura Cloud | AWS Managed | Azure Managed |
| Scalability | Vertical + Causal Cluster | Horizontal Auto-scaling | Horizontal Auto-scaling |
| ACID Compliance | Full ACID | Full ACID | Tunable Consistency |
| Graph Algorithms | GDS Library (60+) | Limited Built-in | Limited Built-in |
| Cost (1TB) | $2,000-$4,000/mo | $1,500-$3,000/mo | $1,800-$3,500/mo |
AI-Powered vs Manual Graph Migration
| Factor | AI-Powered Migration | Manual Migration |
|---|---|---|
| Timeline | 2-3 weeks | 2-4 months |
| Relationship Preservation | 100% automated validation | 95-98% manual checks |
| Query Conversion | 95% automated (Cypher/Gremlin) | Manual rewrite required |
| Downtime | Zero (dual-write pattern) | 4-8 hours maintenance window |
| Cost | $75K-$150K | $300K-$600K |
| Performance Optimization | AI-optimized indexes | Manual tuning required |
| Risk Level | Low (automated validation) | Medium-High (human error) |
People Also Ask
What is graph database migration?
Graph database migration is the process of moving graph data (nodes, relationships, properties) from one graph database platform to another while preserving 100% of relationship integrity. This includes migrating graph schemas, converting query languages (Cypher to Gremlin), updating application drivers, and optimizing performance for the target platform. AI-powered migration achieves this in 2-3 weeks with zero downtime.
How does AI ensure 100% relationship preservation?
AI agents validate every relationship during migration by comparing source and target graph structures. They verify node IDs, relationship types, directionality, and properties using graph traversal algorithms. Any discrepancies trigger automatic reconciliation. The system performs continuous validation during migration and final comprehensive checks before cutover, ensuring zero relationship loss.
Can you convert Cypher queries to Gremlin automatically?
Yes, AI agents achieve 95% automated conversion of Cypher queries to Gremlin. They analyze query patterns, graph traversals, and filtering logic to generate equivalent Gremlin code. Complex queries with custom procedures may require manual review, but the AI handles standard MATCH, WHERE, RETURN patterns, aggregations, and path queries automatically. The system also optimizes converted queries for target platform performance.
How long does graph database migration take?
AI-powered graph migration typically takes 2-3 weeks for most enterprise deployments. This includes graph analysis (3 days), schema mapping (4 days), data migration (7 days), and cutover/optimization (7 days). Manual migration takes 2-4 months due to complex relationship validation and query conversion requirements. Timeline varies based on graph size, complexity, and number of relationship types.
What are the costs of graph database migration?
AI-powered graph migration costs $75K-$150K for typical enterprise deployments, compared to $300K-$600K for manual migration (75% savings). Costs include graph analysis, schema mapping, data migration, query conversion, application updates, and performance optimization. Additional savings come from reduced downtime (zero vs 4-8 hours), faster time-to-value (weeks vs months), and lower risk of relationship data loss.
Ready to Migrate Your Graph Database?
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