DBMS > Datomic vs. GridGain vs. SAP IQ vs. Snowflake vs. Spark SQL

System Properties Comparison Datomic vs. GridGain vs. SAP IQ vs. Snowflake vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

Columnar RDBMS optimized for Big Data analytics

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

column-oriented

Relational DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

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

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.datomic.com

www.gridgain.com

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

www.snowflake.com

spark.apache.org/sql

Technical documentation

docs.datomic.com

www.gridgain.com/docs/index.html

help.sap.com/docs/SAP_IQ

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Cognitect

GridGain Systems, Inc.

SAP, formerly Sybase

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2012

2007

1994

2014

Current release

1.0.6735, June 2023

GridGain 8.5.1

16.1 SPS04, April 2019

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Apache 2.0

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, Clojure

Java, C++, .Net

Scala

Server operating systems

All OS with a Java VM

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
Solaris
Windows

hosted

Linux
OS X
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

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

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

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

ADO.NET
JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Clojure
Java

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Java
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

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

yes

user defined functions

Triggers

By using transaction functions

yes (cache interceptors and events)

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes (replicated cache)

SAP/Sybase Replication Server

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Hadoop integration

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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

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