DBMS > Apache Phoenix vs. Google BigQuery vs. IBM Db2 vs. Linter vs. SAP Adaptive Server

System Properties Comparison Apache Phoenix vs. Google BigQuery vs. IBM Db2 vs. Linter vs. SAP Adaptive Server

Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Large scale data warehouse service with append-only tables

Common in IBM host environments, 2 different versions for host and Windows/Linux

RDBMS for high security requirements

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Primary database model

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	2.02
Rank	#130	Overall
	#63	Relational DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	127.49
Rank	#8	Overall
	#5	Relational DBMS

Score	0.10
Rank	#350	Overall
	#153	Relational DBMS

Score	37.12
Rank	#25	Overall
	#17	Relational DBMS

Website

phoenix.apache.org

cloud.google.com/bigquery

www.ibm.com/products/db2

linter.ru

www.sap.com/products/technology-platform/sybase-ase.html

Technical documentation

phoenix.apache.org

cloud.google.com/bigquery/docs

www.ibm.com/docs/en/db2

help.sap.com/docs/SAP_ASE

Developer

Apache Software Foundation

Google

IBM

relex.ru

SAP, Sybase

Initial release

2014

2010

1983

host version

1990

1987

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

12.1, October 2016

16.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free version is available

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

Java

C and C++

Server operating systems

Linux
Unix
Windows

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

AIX
HP-UX
Linux
Solaris
Unix
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

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

APIs and other access methods

JDBC

RESTful HTTP/JSON API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

C
C++
Cobol
Java
Java
Perl
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax with the possibility to convert from PL/SQL

Java and Transact-SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

only with Windows/Unix/Linux Version

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

with separate tools (MQ, InfoSphere)

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual 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

ACID

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.

yes

User concepts

Access control

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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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