DBMS > Google BigQuery vs. GridGain vs. Oracle Rdb vs. Postgres-XL vs. WakandaDB

System Properties Comparison Google BigQuery vs. GridGain vs. Oracle Rdb vs. Postgres-XL vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

GridGain is an in-memory computing platform, built on Apache Ignite

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

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Object oriented DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

cloud.google.com/bigquery

www.gridgain.com

www.oracle.com/database/technologies/related/rdb.html

www.postgres-xl.org

wakanda.github.io

Technical documentation

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

www.oracle.com/database/technologies/related/rdb-doc.html

www.postgres-xl.org/documentation

wakanda.github.io/doc

Developer

Google

GridGain Systems, Inc.

Oracle, originally developed by Digital Equipment Corporation (DEC)

Wakanda SAS

Initial release

2010

2007

1984

2014

since 2012, originally named StormDB

2012

Current release

GridGain 8.5.1

7.4.1.1, 2021

10 R1, October 2018

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Mozilla public license

Open Source

AGPLv3, extended commercial license available

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

Java, C++, .Net

C++, JavaScript

Server operating systems

hosted

Linux
OS X
Solaris
Windows

HP Open VMS

Linux
macOS

Linux
OS X
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

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

yes

distributed, parallel query execution

APIs and other access methods

RESTful HTTP/JSON API

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

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

RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

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

JavaScript

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

user defined functions

yes

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Since BigQuery is designed for querying data

ACID

yes, on a single node

ACID

MVCC

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)

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

yes

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