DBMS > Apache Druid vs. EXASOL vs. GridGain vs. Snowflake vs. Transbase

System Properties Comparison Apache Druid vs. EXASOL vs. GridGain vs. Snowflake vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

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

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

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

Primary database model

Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

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Trend Chart

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

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

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

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

Technical documentation

druid.apache.org/docs/latest/design

www.exasol.com/resources

www.gridgain.com/docs/index.html

docs.snowflake.net/manuals/index.html

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

Developer

Apache Software Foundation and contributors

Exasol

GridGain Systems, Inc.

Snowflake Computing Inc.

Transaction Software GmbH

Initial release

2012

2000

2007

2014

1987

Current release

29.0.1, April 2024

GridGain 8.5.1

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache license v2

commercial

free development license

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

Java, C++, .Net

C and C++

Server operating systems

Linux
OS X
Unix

Linux
OS X
Solaris
Windows

hosted

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-less columns are supported

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

SQL for querying

yes

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

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

.Net
JDBC
ODBC
WebSocket

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

CLI Client
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Java
Lua
Python
R

C#
C++
Java
PHP
Python
Ruby
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)

user defined functions

yes

Triggers

yes

yes (cache interceptors and events)

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes (replicated cache)

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

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

Security Hooks for custom implementations

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

fine grained access rights according to SQL-standard

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