DBMS > GBase vs. Google BigQuery vs. Greenplum vs. Ignite vs. SAP HANA

System Properties Comparison GBase vs. Google BigQuery vs. Greenplum vs. Ignite vs. SAP HANA

Editorial information provided by DB-Engines

Name

Description

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Large scale data warehouse service with append-only tables

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational 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	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	8.08
Rank	#48	Overall
	#31	Relational DBMS

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

www.gbase.cn

cloud.google.com/bigquery

greenplum.org

ignite.apache.org

www.sap.com/products/hana.html

Technical documentation

cloud.google.com/bigquery/docs

docs.greenplum.org

apacheignite.readme.io/docs

help.sap.com/hana

Developer

General Data Technology Co., Ltd.

Google

Pivotal Software Inc.

Apache Software Foundation

SAP

Initial release

2004

2010

2005

2015

2010

Current release

GBase 8a, GBase 8s, GBase 8c

7.0.0, September 2023

Apache Ignite 2.6

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

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, Java, Python

C++, Java, .Net

Server operating systems

Linux

hosted

Linux

Linux
OS X
Solaris
Windows

Appliance or cloud-service

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

since Version 4.2

yes

Secondary indexes

yes

SQL

Support of SQL

Standard with numerous extensions

yes

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

RESTful HTTP/JSON API

JDBC
ODBC

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
Java
Perl
Python
R

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

yes (compute grid and cache interceptors can be used instead)

SQLScript, R

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes (replicated cache)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

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

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

yes

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

Security Hooks for custom implementations

yes

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