DBMS > Google BigQuery vs. SAP SQL Anywhere vs. TigerGraph

System Properties Comparison Google BigQuery vs. SAP SQL Anywhere vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Relational DBMS

Graph DBMS

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Trend Chart

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	4.57
Rank	#78	Overall
	#43	Relational DBMS

Score	1.83
Rank	#141	Overall
	#13	Graph DBMS

Website

cloud.google.com/bigquery

www.sap.com/products/technology-platform/sql-anywhere.html

www.tigergraph.com

Technical documentation

cloud.google.com/bigquery/docs

help.sap.com/docs/SAP_SQL_Anywhere

docs.tigergraph.com

Developer

Google

SAP

formerly Sybase

Initial release

2010

1992

2017

Current release

17, July 2015

License

Commercial or Open Source

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

C++

Server operating systems

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (GSQL)

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
HTTP API
JDBC
ODBC

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in C/C++, Java, .Net or Perl

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Database mirroring

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

Relationships in graphs

Transaction concepts

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

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Role-based access control

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