DBMS > Google BigQuery vs. openGauss vs. SAP HANA vs. Warp 10

System Properties Comparison Google BigQuery vs. openGauss vs. SAP HANA vs. Warp 10

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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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial 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	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

cloud.google.com/bigquery

gitee.com/opengauss
opengauss.org

www.sap.com/products/hana.html

www.warp10.io

Technical documentation

cloud.google.com/bigquery/docs

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

help.sap.com/hana

www.warp10.io/content/02_Getting_started

Developer

Google

Huawei and openGauss community

SAP

SenX

Initial release

2010

2019

2010

2015

Current release

3.0, March 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

commercial

Open Source

Apache License 2.0

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

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Implementation language

C, C++, Java

Java

Server operating systems

hosted

Linux

Appliance or cloud-service

Linux
OS X
Windows

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.

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL 2011

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

SQLScript, R

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by range, list and hash)

yes

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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