DBMS > Google BigQuery vs. OpenEdge vs. OrigoDB vs. Riak KV vs. Sadas Engine

System Properties Comparison Google BigQuery vs. OpenEdge vs. OrigoDB vs. Riak KV vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Application development environment with integrated database management system

A fully ACID in-memory object graph database

Distributed, fault tolerant key-value store

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

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Trend Chart

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

cloud.google.com/bigquery

www.progress.com/openedge

origodb.com

www.sadasengine.com

Technical documentation

cloud.google.com/bigquery/docs

documentation.progress.com/output/ua/OpenEdge_latest

origodb.com/docs

www.tiot.jp/riak-docs/riak/kv/latest

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Google

Progress Software Corporation

Robert Friberg et al

OpenSource, formerly Basho Technologies

SADAS s.r.l.

Initial release

2010

1984

2009

under the name LiveDB

2009

2006

Current release

OpenEdge 12.2, March 2020

3.2.0, December 2022

8.0

License

Commercial or Open Source

commercial

Open Source

Apache version 2, commercial enterprise edition

commercial

free trial version 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

Erlang

C++

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows

Linux
Windows

Linux
OS X

AIX
Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

close to SQL 92

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

.NET Client API
HTTP API
LINQ

HTTP API
Native Erlang Interface

JDBC
ODBC
Proprietary protocol

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Progress proprietary ABL (Advanced Business Language)

.Net

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Erlang

Triggers

yes

Domain Events

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

since Version 11.4

horizontal partitioning

client side managed; servers are not synchronized

Sharding

no "single point of failure"

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

links between data sets can be stored

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

Write ahead log

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

managed by 'Learn by Usage'

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users and groups

Role based authorization

yes, using Riak Security

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

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