DBMS > EXASOL vs. GridGain vs. JanusGraph vs. Snowflake vs. Splunk

System Properties Comparison EXASOL vs. GridGain vs. JanusGraph vs. Snowflake vs. Splunk

Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

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

Analytics Platform for Big Data

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Graph DBMS

Relational DBMS

Search engine

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

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	86.45
Rank	#14	Overall
	#2	Search engines

Website

Technical documentation

www.exasol.com/resources

www.gridgain.com/docs/index.html

docs.janusgraph.org

docs.snowflake.net/manuals/index.html

docs.splunk.com/Documentation/Splunk

Developer

Exasol

GridGain Systems, Inc.

Linux Foundation; originally developed as Titan by Aurelius

Snowflake Computing Inc.

Splunk Inc.

Initial release

2000

2007

2017

2014

2003

Current release

GridGain 8.5.1

0.6.3, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Limited free edition and free developer edition available

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

Java, C++, .Net

Java

Server operating systems

Linux
OS X
Solaris
Windows

Linux
OS X
Unix
Windows

hosted

Linux
OS X
Solaris
Windows

Data scheme

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

yes

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

yes

Splunk Search Processing Language for search commands

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

CLI Client
JDBC
ODBC

HTTP REST

Supported programming languages

Java
Lua
Python
R

C#
C++
Java
PHP
Python
Ruby
Scala

Clojure
Java
Python

JavaScript (Node.js)
Python

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

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

yes

user defined functions

yes

Triggers

yes

yes (cache interceptors and events)

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes (compute grid and hadoop accelerator)

yes

via Faunus, a graph analytics engine

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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

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, groups and roles according to SQL-standard

Security Hooks for custom implementations

User authentification and security via Rexster Graph Server

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

Access rights for users and roles

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