DBMS > Google Cloud Datastore vs. Kinetica vs. Microsoft SQL Server vs. ObjectBox vs. Teradata

System Properties Comparison Google Cloud Datastore vs. Kinetica vs. Microsoft SQL Server vs. ObjectBox vs. Teradata

Editorial information provided by DB-Engines

Name

Description

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

Fully vectorized database across both GPUs and CPUs

Microsofts flagship relational DBMS

Extremely fast embedded database for small devices, IoT and Mobile

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

cloud.google.com/datastore

www.kinetica.com

www.microsoft.com/en-us/sql-server

objectbox.io

www.teradata.com

Technical documentation

cloud.google.com/datastore/docs

docs.kinetica.com

learn.microsoft.com/en-US/sql/sql-server

docs.objectbox.io

docs.teradata.com

Developer

Google

Kinetica

Microsoft

ObjectBox Limited

Teradata

Initial release

2008

2012

1989

2017

1984

Current release

7.1, August 2021

SQL Server 2022, November 2022

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache License 2.0

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, C++

C++

C and C++

Server operating systems

hosted

Linux

Linux
Windows

Android
iOS
Linux
macOS
Windows

hosted
Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like DML and DDL statements

yes

SQL 2016 + extensions

APIs and other access methods

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

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Proprietary native API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

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

C++
Java
JavaScript (Node.js)
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

UDFs, stored procedures, table functions in parallel

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Source-replica replication

yes, but depending on the SQL-Server Edition

online/offline synchronization between client and server

Multi-source replication
Source-replica replication

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 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

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

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

GPU vRAM or System RAM

yes

User concepts

Access control

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

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

News

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

Vector Database Release for Flutter / Dart + Python
13 June 2023

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