Estimated reading time: 3 minutes

AWS DynamoDB vs Azure CosmosDB vs GCP Bigtable & Firestore

AWS NoSQL vs Azure NoSQL vs GCP NoSQL

AWS NoSQL vs Azure NoSQL vs GCP NoSQL

FeatureAmazon DynamoDBAzure Cosmos DBGoogle Cloud FirestoreGoogle Cloud Bigtable
Data ModelPrimarily Key-Value and DocumentMulti-model: Document, Key-Value, Wide-Column (Cassandra API), Graph (Gremlin API), Table (Table API)Document-orientedWide-column (Column-family)
ScalabilityHighly scalable, automatic partitioning (Partitioning)Massively scalable, automatic and instant scalability with global distribution options (Partitioning)Highly scalable, automatic scaling (Add Data – scaling is implicit)Massively scalable, horizontal scaling via nodes (Scalability)
Global DistributionMulti-region, multi-active with Global TablesBuilt-in global distribution with multi-region writes (multi-master) available (Global Data Distribution)Multi-region with strong consistency options (Locations)Global replication available (Replication Overview)
ConsistencyTunable (Read Consistency): Eventual, StrongFive well-defined models (Consistency Levels): Strong, Bounded Staleness, Session, Consistent Prefix, EventualTunable (Transactions – implies strong), EventualStrong consistency per row (Consistency)
QueryingKey-based lookups, Scan, Query API with limited filteringSQL-like (SQL Query) for document model, API-specific for othersRich querying with indexing, including compound indexesKey-based lookups, range scans, filtering on columns
TransactionsACID transactions (multi-item) (Transactions)ACID transactions across documents within a partition (Transactions)ACID transactions (multi-document) (Transactions)Transactions within a single row (Transactions)
ServerlessFully serverless, auto-scaling to zero (On-Demand Pricing)Fully managed with serverless options and provisioned throughput with auto-scalingFully serverless, pay-per-use (Firebase Pricing)Not fully serverless, billed by node hours (Pricing)
APIs/EcosystemAWS ecosystem integration (AWS)Broad API support, strong Azure integration (Azure), integration with Microsoft FabricGCP ecosystem integration (Google Cloud), Firebase integrationGCP ecosystem integration (Google Cloud), HBase compatibility
PricingProvisioned (Provisioned Pricing) and On-demand based on RCUs/WCUs and storageProvisioned Throughput (Provisioned Pricing) and Serverless based on request units, storage, and bandwidthPay-per-read, pay-per-write, pay-per-storage (Firebase Pricing)Billed by node hours, storage, and network egress (Pricing)
IndexingPrimary key (partition and sort key), Global Secondary Indexes (GSIs), Local Secondary Indexes (LSIs)Automatic indexing of all attributes by default, with options to customize (Index Policy)Automatic indexing with options for composite and single-field indexesRow key, column families, and qualifiers are indexed (Schema Design – key design is crucial for querying)
Managed ServicesFully managed (Features)Fully managed (Overview), handles patching, upgrades, backups, global replicationFully managed (Overview), handles patching, upgrades, backups, multi-regionFully managed (Overview), handles patching, upgrades, backups, replication

Key Differences and Considerations:

  • Data Model Flexibility: Azure Cosmos DB (Introduction) offers the most built-in flexibility with its multi-model support.
  • Querying Power: Cosmos DB’s SQL-like querying (SQL Query) and Firestore’s rich indexing (Firestore Indexing) provide powerful ways to retrieve data. DynamoDB’s querying (Query API) is more structured. Bigtable’s querying (Reads) is focused on key-based and range scans.
  • Global Distribution: Both Cosmos DB (Global Data Distribution) and DynamoDB (Global Tables) offer robust global distribution. Firestore (Locations) provides multi-region options, and Bigtable (Replication Overview) offers global replication.
  • Serverless Nature: DynamoDB (On-Demand Pricing), Azure Cosmos DB (Serverless), and Cloud Firestore (Firebase Pricing) offer serverless options. Bigtable (Pricing) requires managing nodes.
  • Use Cases: The best choice depends on your specific application needs. Refer to the documentation links in the table for typical use cases of each service.
  • Ecosystem Integration: Consider your existing cloud provider and the ease of integration with other services.

Agentic AI (45) AI (2) AI Agent (25) airflow (3) Algorithm (45) Algorithms (108) apache (32) apex (11) API (118) Automation (68) Autonomous (84) auto scaling (5) AWS (63) aws bedrock (1) Azure (56) Banks (1) BigQuery (23) bigtable (3) blockchain (9) Career (9) Chatbot (26) cloud (166) cpu (54) cuda (13) Cybersecurity (30) database (89) Databricks (20) Data structure (22) Design (109) dynamodb (12) ELK (3) embeddings (49) emr (3) Finance (4) flink (10) gcp (21) Generative AI (40) gpu (41) graph (57) graph database (15) graphql (3) Healthcare (2) image (87) indexing (40) interview (11) java (45) json (39) Kafka (20) LLM (51) LLMs (75) market analysis (2) Market report (1) market summary (2) Mcp (6) monitoring (130) Monolith (3) mulesoft (8) N8n (9) Networking (18) NLU (5) node.js (19) Nodejs (3) nosql (22) Optimization (104) performance (254) Platform (149) Platforms (124) postgres (5) productivity (39) programming (71) pseudo code (1) python (89) pytorch (33) Q&A (4) RAG (51) rasa (5) rdbms (6) ReactJS (1) realtime (2) redis (11) Restful (7) rust (3) S3 (1) salesforce (25) Spark (32) spring boot (4) sql (79) stock (14) stock analysis (1) stock market (2) tensor (15) time series (17) tips (11) tricks (20) undervalued stocks (2) use cases (144) vector (73) vector db (8) Vertex AI (23) Workflow (68)