DBMS > Google Cloud Datastore vs. ObjectBox vs. Stardog vs. Vertica

System Properties Comparison Google Cloud Datastore vs. ObjectBox vs. Stardog vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

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

Extremely fast embedded database for small devices, IoT and Mobile

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

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

Column oriented

Secondary database models

Time Series DBMS

Spatial DBMS
Time Series DBMS

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

Score	4.49
Rank	#79	Overall
	#12	Document stores

Score	1.22
Rank	#172	Overall
	#5	Object oriented DBMS

Score	2.05
Rank	#129	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	11.40
Rank	#43	Overall
	#27	Relational DBMS

Website

cloud.google.com/datastore

objectbox.io

www.stardog.com

www.vertica.com

Technical documentation

cloud.google.com/datastore/docs

docs.objectbox.io

docs.stardog.com

vertica.com/documentation

Developer

Google

ObjectBox Limited

Stardog-Union

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2008

2017

2010

2005

Current release

7.3.0, May 2020

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

C and C++

Java

C++

Server operating systems

hosted

Android
iOS
Linux
macOS
Windows

Linux
macOS
Windows

Linux

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

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, 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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

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

SQL

Support of SQL

SQL-like query language (GQL)

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

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

APIs and other access methods

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

Proprietary native API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

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

character-based, interactive, front-end utility

Supported programming languages

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

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

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

Server-side scripts

Stored procedures

using Google App Engine

user defined functions and aggregates, HTTP Server extensions in Java

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

Callbacks using the Google Apps Engine

yes

via event handlers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

online/offline synchronization between client and server

Multi-source replication in HA-Cluster

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

using Google Cloud Dataflow

Bi-directional Spark integration

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

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

relationships in graphs

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

User concepts

Access control

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

yes

Access rights for users and roles

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,...
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Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing models

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

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

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