DBMS > Amazon DocumentDB vs. GridGain vs. Sadas Engine vs. Snowflake vs. Yaacomo

System Properties Comparison Amazon DocumentDB vs. GridGain vs. Sadas Engine vs. Snowflake vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

GridGain is an in-memory computing platform, built on Apache Ignite

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.00
Rank	#385	Overall
	#159	Relational DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

aws.amazon.com/documentdb

www.gridgain.com

www.sadasengine.com

www.snowflake.com

yaacomo.com

Technical documentation

aws.amazon.com/documentdb/resources

www.gridgain.com/docs/index.html

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.snowflake.net/manuals/index.html

Developer

GridGain Systems, Inc.

SADAS s.r.l.

Snowflake Computing Inc.

Q2WEB GmbH

Initial release

2019

2007

2006

2014

2009

Current release

GridGain 8.5.1

8.0

License

Commercial or Open Source

commercial

commercial, open source

commercial

free trial version available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net, Python, REST, SQL

C++

Server operating systems

hosted

Linux
OS X
Solaris
Windows
z/OS

AIX
Linux
Windows

hosted

Android
Linux
Windows

Data scheme

schema-free

yes

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

yes

Typing

predefined data types such as float or date

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

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC
Proprietary protocol

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Groovy
Java
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

user defined functions

Triggers

yes (cache interceptors and events)

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes (replicated cache)

none

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

ACID

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

managed by 'Learn by Usage'

yes

User concepts

Access control

Access rights for users and roles

Role-based access control
Security Hooks for custom implementations

Access rights for users, groups and roles according to SQL-standard

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

fine grained access rights according to SQL-standard

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