DBMS > Amazon Redshift vs. Google Cloud Datastore vs. Kinetica vs. SAP SQL Anywhere vs. SingleStore

System Properties Comparison Amazon Redshift vs. Google Cloud Datastore vs. Kinetica vs. SAP SQL Anywhere vs. SingleStore

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Fully vectorized database across both GPUs and CPUs

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Relational DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	3.50
Rank	#81	Overall
	#44	Relational DBMS

Score	4.02
Rank	#74	Overall
	#39	Relational DBMS

Website

aws.amazon.com/redshift

cloud.google.com/datastore

www.kinetica.com

www.sap.com/products/technology-platform/sql-anywhere.html

www.singlestore.com

Technical documentation

docs.aws.amazon.com/redshift

cloud.google.com/datastore/docs

docs.kinetica.com

help.sap.com/docs/SAP_SQL_Anywhere

docs.singlestore.com

Developer

Amazon (based on PostgreSQL)

Google

Kinetica

SAP

formerly Sybase

SingleStore Inc.

Initial release

2012

2008

2012

1992

2013

Current release

7.1, August 2021

17, July 2015

8.5, January 2024

License

Commercial or Open Source

commercial

free developer edition available

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++

C++, Go

Server operating systems

hosted

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux

64 bit version required

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

SQL-like query language (GQL)

SQL-like DML and DDL statements

yes

but no triggers and foreign keys

APIs and other access methods

JDBC
ODBC

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

JDBC
ODBC
RESTful HTTP API

ADO.NET
HTTP API
JDBC
ODBC

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

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

C++
Java
JavaScript (Node.js)
Python

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

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

using Google App Engine

user defined functions

yes, in C/C++, Java, .Net or Perl

yes

Triggers

Callbacks using the Google Apps Engine

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

Source-replica replication

Database mirroring

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

can define user-defined aggregate functions for map-reduce-style calculations

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 or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

via ReferenceProperties or Ancestor paths

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

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

In-memory capabilities

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

yes

GPU vRAM or System RAM

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 and roles on table level

fine grained access rights according to SQL-standard

Fine grained access control via users, groups and roles

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