DBMS > Google BigQuery vs. Graph Engine vs. Rockset vs. VelocityDB

System Properties Comparison Google BigQuery vs. Graph Engine vs. Rockset vs. VelocityDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A scalable, reliable search and analytics service in the cloud, built on RocksDB

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Document store

Graph DBMS
Object oriented DBMS

Secondary database models

Relational DBMS
Search engine

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

cloud.google.com/bigquery

www.graphengine.io

rockset.com

velocitydb.com

Technical documentation

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

docs.rockset.com

velocitydb.com/UserGuide

Developer

Google

Microsoft

Rockset

VelocityDB Inc

Initial release

2010

2019

2011

Current release

7.x

License

Commercial or Open Source

commercial

Open Source

MIT License

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

.NET and C

C++

Server operating systems

hosted

.NET

hosted

Any that supports .NET

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

dynamic typing

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.

ingestion from XML files supported

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

Read-only SQL queries, including JOINs

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

HTTP REST

.Net

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

Go
Java
JavaScript (Node.js)
Python

.Net

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Automatic sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and organizations can be defined via Rockset console

Based on Windows Authentication

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