DBMS > Apache Kylin vs. Google BigQuery vs. MaxDB vs. Spark SQL vs. Transbase

System Properties Comparison Apache Kylin vs. Google BigQuery vs. MaxDB vs. Spark SQL vs. Transbase

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Large scale data warehouse service with append-only tables

A robust and reliable RDBMS optimized to run all major SAP solutions

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

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

Primary database model

Relational DBMS

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.26
Rank	#114	Overall
	#55	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

kylin.apache.org

cloud.google.com/bigquery

maxdb.sap.com

spark.apache.org/sql

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

Technical documentation

kylin.apache.org/docs

cloud.google.com/bigquery/docs

maxdb.sap.com/documentation

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

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

Developer

Apache Software Foundation, originally contributed from eBay Inc

Google

SAP, acquired from Software AG (Adabas-D) in 1997

Apache Software Foundation

Transaction Software GmbH

Initial release

2015

2010

1984

2014

1987

Current release

3.1.0, July 2020

7.9.10.12, February 2024

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Limited community edition free

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

C++

Scala

C and C++

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

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

ANSI SQL for queries (using Apache Calcite)

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
WebDAV

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
Java
Perl
PHP
Python

Java
Python
R
Scala

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

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

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

yes

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

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)

fine grained access rights according to SQL-standard

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