DBMS > Google BigQuery vs. HBase vs. OpenEdge vs. Oracle Rdb vs. Vitess

System Properties Comparison Google BigQuery vs. HBase vs. OpenEdge vs. Oracle Rdb vs. Vitess

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Wide-column store based on Apache Hadoop and on concepts of BigTable

Application development environment with integrated database management system

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

Wide column store

Relational 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	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	3.45
Rank	#85	Overall
	#46	Relational DBMS

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

cloud.google.com/bigquery

hbase.apache.org

www.progress.com/openedge

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

vitess.io

Technical documentation

cloud.google.com/bigquery/docs

hbase.apache.org/book.html

documentation.progress.com/output/ua/OpenEdge_latest

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

vitess.io/docs

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Progress Software Corporation

Oracle, originally developed by Digital Equipment Corporation (DEC)

The Linux Foundation, PlanetScale

Initial release

2010

2008

1984

2013

Current release

2.3.4, January 2021

OpenEdge 12.2, March 2020

7.4.1.1, 2021

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

AIX
HP-UX
Linux
Solaris
Windows

HP Open VMS

Docker
Linux
macOS

Data scheme

yes

schema-free, schema definition possible

yes

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

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

close to SQL 92

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

JDBC
ODBC

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

Progress proprietary ABL (Advanced Business Language)

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Coprocessors in Java

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

since Version 11.4

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Since BigQuery is designed for querying data

Single row ACID (across millions of columns)

ACID

yes, on a single node

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Users and groups

Users with fine-grained authorization concept

no user groups or roles

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