DBMS > Google BigQuery vs. Postgres-XL vs. SAP Adaptive Server vs. YottaDB

System Properties Comparison Google BigQuery vs. Postgres-XL vs. SAP Adaptive Server vs. YottaDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

A fast and solid embedded Key-value store

Primary database model

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

with Boeing's Spatial Query Server

Relational DBMS

using the Octo plugin

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	35.09
Rank	#25	Overall
	#17	Relational DBMS

Score	0.28
Rank	#306	Overall
	#44	Key-value stores

Website

cloud.google.com/bigquery

www.postgres-xl.org

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

yottadb.com

Technical documentation

cloud.google.com/bigquery/docs

www.postgres-xl.org/documentation

help.sap.com/docs/SAP_ASE

yottadb.com/resources/documentation

Developer

Google

SAP, Sybase

YottaDB, LLC

Initial release

2010

2014

since 2012, originally named StormDB

1987

2001

Current release

10 R1, October 2018

16.0

License

Commercial or Open Source

commercial

Open Source

Mozilla public license

commercial

Open Source

AGPL 3.0

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 and C++

Server operating systems

hosted

Linux
macOS

AIX
HP-UX
Linux
Solaris
Unix
Windows

Docker
Linux

Data scheme

yes

schema-free

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

distributed, parallel query execution

yes

by using the Octo plugin

APIs and other access methods

RESTful HTTP/JSON API

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

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

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C++
Cobol
Java
Java
Perl
PHP
Python

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

Java and Transact-SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

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

MVCC

ACID

optimistic locking

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Users and groups based on OS-security mechanisms

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