DBMS > Google BigQuery vs. openGauss vs. SAP HANA vs. Teradata

System Properties Comparison Google BigQuery vs. openGauss vs. SAP HANA vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

In-memory, column based data store. Available as appliance or cloud service

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.06
Rank	#184	Overall
	#84	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

cloud.google.com/bigquery

gitee.com/opengauss
opengauss.org

www.sap.com/products/hana.html

www.teradata.com

Technical documentation

cloud.google.com/bigquery/docs

docs.opengauss.org/en
gitee.com/opengauss/docs

help.sap.com/hana

docs.teradata.com

Developer

Google

Huawei and openGauss community

SAP

Teradata

Initial release

2010

2019

2010

1984

Current release

3.0, March 2022

2.0 SPS07 (April 4, 2023), April 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++, Java

Server operating systems

hosted

Linux

Appliance or cloud-service

hosted
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

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

ANSI SQL 2011

yes

SQL 2016 + extensions

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Java

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

SQLScript, R

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by range, list and hash)

yes

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication
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

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

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)

Access rights for users, groups and roles according to SQL-standard

yes

fine grained access rights according to SQL-standard

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