DBMS > Google BigQuery vs. Hive vs. Immudb vs. Percona Server for MongoDB vs. PouchDB

System Properties Comparison Google BigQuery vs. Hive vs. Immudb vs. Percona Server for MongoDB vs. PouchDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Relational DBMS

Key-value store

Document store

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	61.17
Rank	#18	Overall
	#12	Relational DBMS

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

Score	0.52
Rank	#254	Overall
	#39	Document stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Website

cloud.google.com/bigquery

hive.apache.org

github.com/codenotary/immudb
immudb.io

www.percona.com/mongodb/software/percona-server-for-mongodb

pouchdb.com

Technical documentation

cloud.google.com/bigquery/docs

cwiki.apache.org/confluence/display/Hive/Home

docs.immudb.io

docs.percona.com/percona-distribution-for-mongodb

pouchdb.com/guides

Developer

Google

Apache Software Foundation

initially developed by Facebook

Codenotary

Percona

Apache Software Foundation

Initial release

2010

2012

2020

2015

2012

Current release

3.1.3, April 2022

1.2.3, April 2022

3.4.10-2.10, November 2017

7.1.1, June 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

GPL Version 2

Open Source

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

C++

JavaScript

Server operating systems

hosted

All OS with a Java VM

BSD
Linux
macOS
Solaris
Windows
z/OS

Linux

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

yes

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.

Secondary indexes

yes

via views

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like syntax

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

gRPC protocol
PostgreSQL wire protocol

proprietary protocol using JSON

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
PHP
Python

.Net
Go
Java
JavaScript (Node.js)
Python

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

JavaScript

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

JavaScript

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

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

by using IndexedDB, WebSQL or LevelDB as backend

In-memory capabilities

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

yes

via In-Memory Engine

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

Access rights for users and roles

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