DBMS > Google Cloud Datastore vs. Kinetica vs. NebulaGraph vs. Snowflake

System Properties Comparison Google Cloud Datastore vs. Kinetica vs. NebulaGraph vs. Snowflake

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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

Fully vectorized database across both GPUs and CPUs

A distributed, linear scalable, high perfomant Graph DBMS

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Secondary database models

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	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	2.12
Rank	#125	Overall
	#10	Graph DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

cloud.google.com/datastore

www.kinetica.com

github.com/vesoft-inc/nebula
www.nebula-graph.io

www.snowflake.com

Technical documentation

cloud.google.com/datastore/docs

docs.kinetica.com

docs.nebula-graph.io

docs.snowflake.net/manuals/index.html

Developer

Google

Kinetica

Vesoft Inc.

Snowflake Computing Inc.

Initial release

2008

2012

2019

2014

Current release

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0 + Common Clause 1.0

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++

Server operating systems

hosted

Linux

hosted

Data scheme

schema-free

yes

Strong typed schema

yes

support of semi-structured data formats (JSON, XML, Avro)

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

Nebula Graph internally uses the Key-Value store RocksDB for persistency. The vertices, edges, and their properties are stored as Key while their values are stored as Value. The primary indexes are per Key and secondary indexes are per Value.

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like DML and DDL statements

SQL-like query language

yes

APIs and other access methods

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

JDBC
ODBC
RESTful HTTP API

Browser interface
console (shell)
Cypher Query Language
GO Object Graph Mapper
Java Object Graph Mapper
NGBatis

ORM framework for NebulaGraph and Spring-Boot
Proprietary native API
Python Object Graph Mapper
Query language nGQL

CLI Client
JDBC
ODBC

Supported programming languages

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

C++
Java
JavaScript (Node.js)
Python

.Net
C++
Go
Java
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

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

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Source-replica replication

Causal Clustering using Raft protocol

yes

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

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

using RocksDB

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

Role-based access control

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

NebulaGraph is a truly distributed, linearly scalable, lightning-fast graph database,...
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Competitive advantages

NebulaGraph boasts the world's only graph database solution that is able to host...
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Typical application scenarios

Social networking Fraud detection Knowledge graph Data warehouse management Anti...
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Key customers

Companies from a variety of industries have implemented NebulaGraph Database in production,...
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Market metrics

At our very early stage, NebulaGraph has already received over 10,000 stars on GitHub...
» more

Licensing and pricing models

NebulaGraph is open source and free to use under Apache 2.0 license.
» more

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