DBMS > Altibase vs. Google Cloud Datastore vs. InfinityDB vs. InterSystems IRIS vs. Snowflake

System Properties Comparison Altibase vs. Google Cloud Datastore vs. InfinityDB vs. InterSystems IRIS vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

A Java embedded Key-Value Store which extends the Java Map interface

A containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalability

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

Primary database model

Relational DBMS

Document store

Key-value store

Document store
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

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

Score	1.07
Rank	#186	Overall
	#87	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	4.05
Rank	#83	Overall
	#14	Document stores
	#10	Key-value stores
	#1	Object oriented DBMS
	#45	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

altibase.com

cloud.google.com/datastore

boilerbay.com

www.intersystems.com/products/intersystems-iris

www.snowflake.com

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

cloud.google.com/datastore/docs

boilerbay.com/infinitydb/manual

docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls

docs.snowflake.net/manuals/index.html

Developer

Altibase

Google

Boiler Bay Inc.

InterSystems

Snowflake Computing Inc.

Initial release

1999

2008

2002

2018

2014

Current release

v7.3, 2023, August 2023

4.0

2023.3, June 2023

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

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++

Java

Server operating systems

AIX
HP-UX
Linux

hosted

All OS with a Java VM

AIX
Linux
macOS
Ubuntu
Windows

hosted

Data scheme

yes

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

depending on used data model

yes

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

Typing

predefined data types such as float or date

yes

yes, details here

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

ANSI SQL-92

SQL-like query language (GQL)

yes

APIs and other access methods

JDBC
ODBC
Proprietary protocol

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C
C++
Java

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

stored procedures and stored functions

using Google App Engine

yes

user defined functions

Triggers

yes

Callbacks using the Google Apps Engine

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

none

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Optimistic locking for transactions; no isolation for bulk loads

ACID

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

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

More information provided by the system vendor

Specific characteristics

InterSystems IRIS is a complete cloud-first data platform which includes a multi-model...
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