DBMS > Google BigQuery vs. Immudb vs. OpenQM vs. Warp 10

System Properties Comparison Google BigQuery vs. Immudb vs. OpenQM vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

An open source immutable (append-only) database with cryptographic verification which makes it tamper-resistant and fully auditable.

QpenQM is a high-performance, self-tuning, multi-value DBMS

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Secondary database models

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.24
Rank	#305	Overall
	#43	Key-value stores

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

cloud.google.com/bigquery

github.com/codenotary/immudb
immudb.io

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

www.warp10.io

Technical documentation

cloud.google.com/bigquery/docs

docs.immudb.io

www.warp10.io/content/02_Getting_started

Developer

Google

Codenotary

Rocket Software, originally Martin Phillips

SenX

Initial release

2010

2020

1993

2015

Current release

1.2.3, April 2022

3.4-12

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPLv2, extended commercial license available

Open Source

Apache License 2.0

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

Server operating systems

hosted

BSD
Linux
macOS
Solaris
Windows
z/OS

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

with some exceptions

schema-free

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

yes

SQL

Support of SQL

yes

SQL-like syntax

APIs and other access methods

RESTful HTTP/JSON API

gRPC protocol
PostgreSQL wire protocol

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
Python

.Net
Basic
C
Java
Objective C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

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

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 rights can be defined down to the item level

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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