DBMS > GridGain vs. JanusGraph vs. Microsoft Access vs. SiriDB vs. Snowflake

System Properties Comparison GridGain vs. JanusGraph vs. Microsoft Access vs. SiriDB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Open Source Time Series DBMS

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

Primary database model

Key-value store
Relational DBMS

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

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

www.gridgain.com

janusgraph.org

www.microsoft.com/en-us/microsoft-365/access

siridb.com

www.snowflake.com

Technical documentation

www.gridgain.com/docs/index.html

docs.janusgraph.org

developer.microsoft.com/en-us/access

docs.siridb.com

docs.snowflake.net/manuals/index.html

Developer

GridGain Systems, Inc.

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

Cesbit

Snowflake Computing Inc.

Initial release

2007

2017

1992

2017

2014

Current release

GridGain 8.5.1

0.6.3, February 2023

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Bundled with Microsoft Office

Open Source

MIT License

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

Java

C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux
OS X
Unix
Windows

Windows

Not a real database server, but making use of DLLs

Linux

hosted

Data scheme

yes

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

Typing

predefined data types such as float or date

yes

Numeric data

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

but not compliant to any SQL standard

yes

APIs and other access methods

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
DAO
ODBC
OLE DB

HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Clojure
Java
Python

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

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

yes

since Access 2010 using the ACE-engine

user defined functions

Triggers

yes (cache interceptors and events)

yes

since Access 2010 using the ACE-engine

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

but no files for transaction logging

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

Security Hooks for custom implementations

User authentification and security via Rexster Graph Server

a simple user-level security was built in till version Access 2003

simple rights management via user accounts

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

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