DBMS > Informix vs. Ingres vs. Microsoft Azure Synapse Analytics vs. Splice Machine vs. SQLite

System Properties Comparison Informix vs. Ingres vs. Microsoft Azure Synapse Analytics vs. Splice Machine vs. SQLite

Editorial information provided by DB-Engines

Name

Description

A secure embeddable database from IBM, positioned besides IBM Db2 as a relatively low-cost product optimized for OLTP and Internet of Things data

Well established RDBMS

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Since Version 12.10 support for JSON/BSON datatypes compatible with MongoDB

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS

with Informix TimeSeries Extension

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

Score	16.32
Rank	#35	Overall
	#21	Relational DBMS

Score	3.63
Rank	#79	Overall
	#43	Relational DBMS

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Website

www.ibm.com/products/informix

www.actian.com/databases/ingres

azure.microsoft.com/services/synapse-analytics

splicemachine.com

www.sqlite.org

Technical documentation

informix.hcldoc.com
www.ibm.com/support/knowledgecenter/SSGU8G/welcomeIfxServers.html

docs.actian.com/ingres

docs.microsoft.com/azure/synapse-analytics

splicemachine.com/how-it-works

www.sqlite.org/docs.html

Developer

IBM, HCL Technologies

Effective May 1st, 2017, HCL took on development, technical support, and product management teams, and works jointly with IBM on product strategy, marketing, and sales.

Actian Corporation

Microsoft

Splice Machine

Dwayne Richard Hipp

Initial release

1984

1974

originally developed at University Berkely in early 1970s

2016

2014

2000

Current release

14.10.FC5, November 2020

12.0, July 2024

3.1, March 2021

3.46.1 (13 August 2024), August 2024

License

Commercial or Open Source

commercial

free developer edition available

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Public Domain

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

C, C++ and Java

C++

Java

Server operating systems

AIX
HP-UX
Linux
macOS
Solaris
Windows

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

hosted

Linux
OS X
Solaris
Windows

server-less

Data scheme

yes

dynamic column types

Typing

predefined data types such as float or date

yes

Since Version 12.10 support for JSON/BSON datatypes

yes

not rigid because of 'dynamic typing' concept.

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
JSON API

MongoDB compatible
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

ADO.NET
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
C
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
Java
PHP

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

Transact SQL

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding, horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Ingres Replicator

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

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

Users with fine-grained authentication, authorization, and auditing controls

fine grained access rights according to SQL-standard

yes

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

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