DBMS > Brytlyt vs. Google BigQuery vs. Spark SQL vs. ToroDB vs. Transbase

System Properties Comparison Brytlyt vs. Google BigQuery vs. Spark SQL vs. ToroDB vs. Transbase

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

Large scale data warehouse service with append-only tables

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

A MongoDB-compatible JSON document store, built on top of PostgreSQL

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

Primary database model

Relational DBMS

Document store

Relational DBMS

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

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

brytlyt.io

cloud.google.com/bigquery

spark.apache.org/sql

github.com/torodb/server

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

Technical documentation

docs.brytlyt.io

cloud.google.com/bigquery/docs

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

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

Developer

Brytlyt

Google

Apache Software Foundation

8Kdata

Transaction Software GmbH

Initial release

2016

2010

2014

2016

1987

Current release

5.0, August 2023

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

AGPL-V3

commercial

free development license

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++ and CUDA

Scala

Java

C and C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X
Windows

All OS with a Java 7 VM

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP/JSON API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
Python
R
Scala

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

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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

fine grained access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

fine grained access rights according to SQL-standard

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