DBMS > Google Cloud Datastore vs. Heroic vs. TigerGraph vs. Valentina Server

System Properties Comparison Google Cloud Datastore vs. Heroic vs. TigerGraph vs. Valentina Server

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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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Object-relational database and reports server

Primary database model

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

Score	4.49
Rank	#79	Overall
	#12	Document stores

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	1.83
Rank	#141	Overall
	#13	Graph DBMS

Score	0.18
Rank	#329	Overall
	#144	Relational DBMS

Website

cloud.google.com/datastore

github.com/spotify/heroic

www.tigergraph.com

www.valentina-db.net

Technical documentation

cloud.google.com/datastore/docs

spotify.github.io/heroic

docs.tigergraph.com

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Google

Spotify

Paradigma Software

Initial release

2008

2014

2017

1999

Current release

5.7.5

License

Commercial or Open Source

commercial

Open Source

Apache 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

Java

C++

Server operating systems

hosted

Linux

Linux
OS X
Windows

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like query language (GSQL)

yes

APIs and other access methods

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

ODBC

Supported programming languages

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

C++
Java

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

using Google App Engine

yes

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

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.

Eventual Consistency
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)

Role-based access control

fine grained access rights according to SQL-standard

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