DBMS > Google BigQuery vs. Infobright vs. OpenEdge vs. SingleStore

System Properties Comparison Google BigQuery vs. Infobright vs. OpenEdge vs. SingleStore

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Application development environment with integrated database management system

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.88
Rank	#198	Overall
	#92	Relational DBMS

Score	3.69
Rank	#78	Overall
	#42	Relational DBMS

Score	4.02
Rank	#74	Overall
	#39	Relational DBMS

Website

cloud.google.com/bigquery

ignitetech.com/softwarelibrary/infobrightdb

www.progress.com/openedge

www.singlestore.com

Technical documentation

cloud.google.com/bigquery/docs

documentation.progress.com/output/ua/OpenEdge_latest

docs.singlestore.com

Developer

Google

Ignite Technologies Inc.; formerly InfoBright Inc.

Progress Software Corporation

SingleStore Inc.

Initial release

2010

2005

1984

2013

Current release

OpenEdge 12.2, March 2020

8.5, January 2024

License

Commercial or Open Source

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

free developer edition 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

C++, Go

Server operating systems

hosted

Linux
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux

64 bit version required

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

Secondary indexes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

yes

close to SQL 92

yes

but no triggers and foreign keys

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

JDBC
ODBC

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Progress proprietary ABL (Advanced Business Language)

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

since Version 11.4

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

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

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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)

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

Users and groups

Fine grained access control via users, groups and roles

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