DBMS > Google BigQuery vs. Spark SQL vs. TerminusDB vs. Transbase vs. XTDB

System Properties Comparison Google BigQuery vs. Spark SQL vs. TerminusDB vs. Transbase vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Document store
RDF store

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

cloud.google.com/bigquery

spark.apache.org/sql

terminusdb.com

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

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

cloud.google.com/bigquery/docs

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

terminusdb.github.io/terminusdb/#

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

www.xtdb.com/docs

Developer

Google

Apache Software Foundation

DataChemist Ltd.

Transaction Software GmbH

Juxt Ltd.

Initial release

2010

2014

2018

1987

2019

Current release

3.5.0 ( 2.13), September 2023

11.0.0, January 2023

Transbase 8.3, 2022

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL V3

commercial

free development license

Open Source

MIT 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

Scala

Prolog, Rust

C and C++

Clojure

Server operating systems

hosted

Linux
OS X
Windows

Linux

FreeBSD
Linux
macOS
Solaris
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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 DML and DDL statements

SQL-like query language (WOQL)

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST
JDBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
Python
R
Scala

JavaScript
Python

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

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Graph Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Journaling Streams

Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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)

Role-based access control

fine grained access rights according to SQL-standard

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