AWS DynamoDB vs DocumentDB vs Neptune

• AWS offers multiple purpose-built NoSQL database services, each optimized for different data models and access patterns.
• DynamoDB is a key-value/document database for high-scale low-latency workloads, DocumentDB is MongoDB-compatible for document workloads, and Neptune is a graph database for highly connected data.
• Choice depends on data model, query patterns, scale requirements, and existing application compatibility.

DynamoDB vs DocumentDB vs Neptune Comparison

Amazon DynamoDB

• Fully serverless key-value and document database – single-digit millisecond latency at any scale.
• Capacity modes: On-Demand (pay per request, zero capacity planning) or Provisioned (with Auto Scaling).
• Designed for massive scale – handles 10+ trillion requests per day, peaks above 100 million requests/second.
• Global Tables – multi-region, multi-active replication with Multi-Region Strong Consistency (MRSC, GA 2025).
• DynamoDB Accelerator (DAX) – in-memory cache for microsecond read latency.
• DynamoDB Streams – capture item-level changes for event-driven processing (Lambda integration).
• TTL – automatic item expiration at no cost.
• Zero-ETL to Redshift – replicate data to Redshift for analytics without pipelines.
• Limitations: 400KB item size, limited query flexibility (must know partition key), no joins, no aggregations.
• Best for: High-scale applications with known access patterns – gaming leaderboards, session stores, IoT, e-commerce carts, serverless backends.

Amazon DocumentDB

• MongoDB-compatible document database – supports MongoDB 3.6, 4.0, and 5.0 API compatibility.
• Purpose-built storage – separates compute from storage (similar to Aurora); storage auto-scales to 128TB.
• Rich queries – full MongoDB query language, aggregation pipelines, secondary indexes, geospatial queries.
• Elastic Clusters – shard collections across multiple nodes for horizontal scaling (millions of reads/writes per second).
• Global Clusters – cross-region disaster recovery with up to 5 read regions.
• Change Streams – MongoDB-compatible change data capture for event-driven architectures.
• 16MB document size – suitable for complex nested documents.
• Not 100% MongoDB compatible – some features differ (check compatibility matrix).
• Best for: MongoDB workloads migrating to AWS, content management, catalogs, user profiles, applications needing flexible schemas with rich querying.

Amazon Neptune

• Fully managed graph database – purpose-built for storing and querying highly connected data.
• Supports three query languages: Apache TinkerPop Gremlin (property graphs), SPARQL (RDF/linked data), and openCypher (declarative graph queries).
• Neptune Analytics – analyze graph data with vector search, graph algorithms, and full-text search.
• Neptune Serverless – automatically scales compute based on workload.
• Neptune ML – machine learning predictions on graph data using GNNs (Graph Neural Networks) via SageMaker.
• Global Database – cross-region read replicas for low-latency reads and disaster recovery.
• Neptune Streams – capture graph changes for downstream processing.
• Up to 15 read replicas – scale reads across multiple instances.
• Best for: Relationship-heavy data – social networks, recommendation engines, fraud detection, knowledge graphs, network topology, identity graphs, supply chain.

When to Choose Which

• Choose DynamoDB when:
  • You need extreme scale with single-digit ms latency
  • Access patterns are well-defined (key-value lookups)
  • You want fully serverless with zero management
  • Use cases: session stores, gaming, IoT, e-commerce, serverless apps
• Choose DocumentDB when:
  • You’re migrating from MongoDB or need MongoDB compatibility
  • Documents are complex/nested and need flexible querying
  • You need aggregation pipelines and secondary indexes
  • Use cases: content management, catalogs, user profiles
• Choose Neptune when:
  • Data is highly connected with complex relationships
  • Queries involve traversing relationships (multi-hop)
  • You need graph algorithms (shortest path, centrality, community detection)
  • Use cases: social networks, fraud detection, knowledge graphs, recommendations

AWS Certification Exam Practice Questions

1. A social media application needs to find “friends of friends” and recommend connections based on mutual relationships. Which database is purpose-built for this query pattern?
   1. DynamoDB with GSI
   2. DocumentDB with aggregation
   3. Neptune (graph traversal)
   4. RDS with JOIN queries
2. A company is migrating a MongoDB application to AWS. They use aggregation pipelines, geospatial queries, and change streams extensively. Which service provides the best compatibility?
   1. DynamoDB with Document model
   2. DocumentDB
   3. Neptune
   4. ElastiCache for MongoDB
3. A gaming application needs a leaderboard that handles 50,000 writes per second with single-digit millisecond latency, using simple key-value access patterns. Which database fits?
   1. DocumentDB
   2. Neptune
   3. Aurora
   4. DynamoDB
4. A fraud detection system needs to analyze transaction patterns by traversing relationships between accounts, devices, IP addresses, and merchants to find suspicious clusters. Which database is best suited?
   1. DynamoDB with Streams
   2. DocumentDB with aggregation
   3. Neptune with graph algorithms
   4. Redshift for analytics
5. An e-commerce application stores product catalogs with deeply nested attributes (variations, specifications, reviews) and needs to query by any attribute with aggregation. Documents average 2MB. Which database fits?
   1. DynamoDB (400KB limit would be exceeded)
   2. DocumentDB (16MB limit, rich queries)
   3. Neptune
   4. S3 with Athena

Related Posts

• AWS Aurora vs RDS – MySQL & PostgreSQL
• AWS Database Services Cheat Sheet
• AWS SQS vs SNS vs EventBridge
• AWS Certified Solutions Architect – Associate Exam Learning Path

References

Amazon DynamoDB Developer Guide

Amazon DocumentDB Developer Guide

Amazon Neptune User Guide