DBMS > Google BigQuery vs. OrientDB vs. Spark SQL vs. TDSQL for MySQL vs. Transbase

System Properties Comparison Google BigQuery vs. OrientDB vs. Spark SQL vs. TDSQL for MySQL vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Multi-model DBMS (Document, Graph, Key/Value)

Spark SQL is a component on top of 'Spark Core' for structured data processing

A high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.85
Rank	#207	Overall
	#97	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

cloud.google.com/bigquery

orientdb.org

spark.apache.org/sql

www.tencentcloud.com/products/dcdb

www.transaction.de/en/products/transbase.html

Technical documentation

cloud.google.com/bigquery/docs

www.orientdb.com/docs/last/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

www.tencentcloud.com/document/product/1042

www.transaction.de/en/products/transbase/features.html

Developer

Google

OrientDB LTD; CallidusCloud; SAP

Apache Software Foundation

Tencent

Transaction Software GmbH

Initial release

2010

2014

2013

1987

Current release

3.2.29, March 2024

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache 2.0

commercial

free development license

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

C and C++

Server operating systems

hosted

All OS with a Java JDK (>= JDK 6)

Linux
OS X
Windows

hosted

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

SQL-like query language, no joins

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP/JSON API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Java
Python
R
Scala

C
C#
C++
Java
PHP
Python
Ruby

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java, Javascript

yes

Triggers

Hooks

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

yes

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.

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 roles; record level security configurable

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

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