DBMS > Amazon DynamoDB vs. CrateDB vs. Google Cloud Datastore vs. Hazelcast vs. Riak TS

System Properties Comparison Amazon DynamoDB vs. CrateDB vs. Google Cloud Datastore vs. Hazelcast vs. Riak TS

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Distributed Database based on Lucene

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

A widely adopted in-memory data grid

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Document store
Key-value store

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Document store

Key-value store

Time Series DBMS

Secondary database models

Relational DBMS

Document store

JSON support with IMDG 3.12

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	77.57
Rank	#16	Overall
	#2	Document stores
	#2	Key-value stores

Score	0.73
Rank	#229	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

Score	4.49
Rank	#79	Overall
	#12	Document stores

Score	6.87
Rank	#55	Overall
	#6	Key-value stores

Score	0.25
Rank	#308	Overall
	#26	Time Series DBMS

Website

aws.amazon.com/dynamodb

cratedb.com

cloud.google.com/datastore

hazelcast.com

Technical documentation

docs.aws.amazon.com/dynamodb

cratedb.com/docs

cloud.google.com/datastore/docs

hazelcast.org/imdg/docs

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Amazon

Crate

Google

Hazelcast

Open Source, formerly Basho Technologies

Initial release

2012

2013

2008

2015

Current release

5.3.6, November 2023

3.0.0, September 2022

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.

Implementation language

Java

Erlang

Server operating systems

hosted

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

hosted

All OS with a Java VM

Linux
OS X

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

the object must implement a serialization strategy

Secondary indexes

yes

restricted

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like query language (GQL)

SQL-like query language

yes, limited

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions (Javascript)

using Google App Engine

yes

Event Listeners, Executor Services

Erlang

Triggers

yes

by integration with AWS Lambda

Callbacks using the Google Apps Engine

yes

Events

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Configurable replication on table/partition-level

Multi-source replication using Paxos

yes

Replicated Map

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

links between datasets can be stored

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

ACID across one or more tables within a single AWS account and region

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

one or two-phase-commit; repeatable reads; read commited

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

rights management via user accounts

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Role-based access control

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
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Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
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Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more

Key customers

Across all continents, CrateDB is used by companies of all sizes to meet the most...
» more

Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
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Licensing and pricing models

See CrateDB pricing >
» more

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