DBMS > GigaSpaces vs. Microsoft Azure Cosmos DB vs. Quasardb vs. SQream DB vs. Vertica

System Properties Comparison GigaSpaces vs. Microsoft Azure Cosmos DB vs. Quasardb vs. SQream DB vs. Vertica

Editorial information provided by DB-Engines

Name

Description

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Globally distributed, horizontally scalable, multi-model database service

Distributed, high-performance timeseries database

a GPU-based, columnar RDBMS for big data analytics workloads

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Column oriented

Secondary database models

Graph DBMS
Search engine

Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Website

www.gigaspaces.com

azure.microsoft.com/services/cosmos-db

quasar.ai

sqream.com

www.vertica.com

Technical documentation

docs.gigaspaces.com/latest/landing.html

learn.microsoft.com/azure/cosmos-db

doc.quasar.ai/master

docs.sqream.com

vertica.com/documentation

Developer

Gigaspaces Technologies

Microsoft

quasardb

SQream Technologies

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2000

2014

2009

2017

2005

Current release

15.5, September 2020

3.14.1, January 2024

2022.1.6, December 2022

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

C++

C++, CUDA, Haskell, Java, Scala

C++

Server operating systems

Linux
macOS
Solaris
Windows

hosted

BSD
Linux
OS X
Windows

Linux

Data scheme

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

JSON types

yes

integer and binary

yes, ANSI Standard SQL Types

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.

XML can be used for describing objects metadata

Secondary indexes

yes

All properties auto-indexed by default

yes

with tags

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL-99 for query and DML statements

SQL-like query language

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

HTTP API

.Net
JDBC
ODBC

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

.Net
C++
Java
Python
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C++
Java
JavaScript (Node.js)
Python

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

JavaScript

user defined functions in Python

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, event driven architecture

JavaScript

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

horizontal and vertical partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

Implicit feature of the cloud service

Source-replica replication with selectable replication factor

none

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

with Hadoop integration

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Role-based access control

Access rights can be defined down to the item level

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more

Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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.