DBMS > Amazon Redshift vs. atoti vs. Google Cloud Datastore vs. GridDB vs. searchxml

System Properties Comparison Amazon Redshift vs. atoti vs. Google Cloud Datastore vs. GridDB vs. searchxml

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

Scalable in-memory time series database optimized for IoT and Big Data

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Native XML DBMS
Search engine

Secondary database models

Key-value store
Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Website

aws.amazon.com/redshift

atoti.io

cloud.google.com/datastore

griddb.net

www.searchxml.net/category/products

Technical documentation

docs.aws.amazon.com/redshift

docs.atoti.io

cloud.google.com/datastore/docs

docs.griddb.net

www.searchxml.net/support/handouts

Developer

Amazon (based on PostgreSQL)

ActiveViam

Google

Toshiba Corporation

informationpartners gmbh

Initial release

2012

2008

2013

2015

Current release

5.1, August 2022

1.0

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

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

Java

C++

Server operating systems

hosted

Linux

Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

numerical, string, blob, geometry, boolean, timestamp

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

Multidimensional Expressions (MDX)

SQL-like query language (GQL)

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

JDBC
ODBC

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

All languages supporting JDBC/ODBC

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

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

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

user defined functions

in Python

Python

using Google App Engine

yes

on the application server

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

Source-replica replication

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

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 within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID at container level

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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)

Access rights for users can be defined per database

Domain, group and role-based access control at the document level and for application services

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.