DBMS > Amazon DocumentDB vs. AnzoGraph DB vs. BigchainDB vs. NCache vs. Snowflake

System Properties Comparison Amazon DocumentDB vs. AnzoGraph DB vs. BigchainDB vs. NCache vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Open-Source and Enterprise in-memory Key-Value Store

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Secondary database models

Document store
Search engine

Using distributed Lucene

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	0.22
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.76
Rank	#216	Overall
	#36	Document stores

Score	0.91
Rank	#196	Overall
	#29	Key-value stores

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

aws.amazon.com/documentdb

cambridgesemantics.com/anzograph

www.bigchaindb.com

www.alachisoft.com/ncache

www.snowflake.com

Technical documentation

aws.amazon.com/documentdb/resources

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

bigchaindb.readthedocs.io/en/latest

www.alachisoft.com/resources/docs

docs.snowflake.net/manuals/index.html

Developer

Cambridge Semantics

Alachisoft

Snowflake Computing Inc.

Initial release

2019

2018

2016

2005

2014

Current release

2.3, January 2021

5.3.3, April 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

AGPL v3

Open Source

Enterprise Edition available

commercial

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.

Implementation language

Python

C#, .NET, .NET Core, Java

Server operating systems

hosted

Linux

Linux
Windows

hosted

Data scheme

schema-free

Schema-free and OWL/RDFS-schema support

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

Secondary indexes

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

CLI Client
RESTful HTTP API

IDistributedCache
JCache
LINQ
Proprietary native API

CLI Client
JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C++
Java
Python

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

support for stored procedures with SQL-Server CLR

user defined functions

Triggers

yes

Notifications

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Automatic sharding

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Multi-source replication in MPP-Cluster

selectable replication factor

yes, with selectable consistency level

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

not needed in graphs

yes

Transaction concepts

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

Atomic single-document operations

ACID

optimistic locking and pessimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

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

yes

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

Bank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
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Market metrics

Market Leader in .NET Distributed Caching since 2005.
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Licensing and pricing models

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