DBMS > EXASOL vs. GridGain vs. HBase vs. Snowflake vs. Transbase

System Properties Comparison EXASOL vs. GridGain vs. HBase vs. Snowflake vs. Transbase

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

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

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Wide column store

Relational DBMS

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

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	1.53
Rank	#155	Overall
	#26	Key-value stores
	#73	Relational DBMS

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

www.transaction.de/en/products/transbase.html

Technical documentation

www.exasol.com/resources

www.gridgain.com/docs/index.html

hbase.apache.org/book.html

docs.snowflake.net/manuals/index.html

www.transaction.de/en/products/transbase/features.html

Developer

Exasol

GridGain Systems, Inc.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Snowflake Computing Inc.

Transaction Software GmbH

Initial release

2000

2007

2008

2014

1987

Current release

GridGain 8.5.1

2.3.4, January 2021

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

free development license

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

C and C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux
Unix
Windows

using Cygwin

hosted

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free, schema definition possible

yes

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

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

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

Java API
RESTful HTTP API
Thrift

CLI Client
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Java
Lua
Python
R

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

JavaScript (Node.js)
Python

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

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

yes

Coprocessors in Java

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Multi-source replication
Source-replica replication

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

ACID

yes

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, groups and roles according to SQL-standard

Security Hooks for custom implementations

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.