DBMS > Google BigQuery vs. Google Cloud Datastore vs. Hive vs. Kyligence Enterprise vs. Percona Server for MongoDB

System Properties Comparison Google BigQuery vs. Google Cloud Datastore vs. Hive vs. Kyligence Enterprise vs. Percona Server for MongoDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

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

A distributed analytics engine for big data, built on top of Apache Kylin

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

Primary database model

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Trend Chart

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Website

cloud.google.com/bigquery

cloud.google.com/datastore

hive.apache.org

kyligence.io/kyligence-enterprise

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

Technical documentation

cloud.google.com/bigquery/docs

cloud.google.com/datastore/docs

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

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

Developer

Google

Apache Software Foundation

initially developed by Facebook

Kyligence, Inc.

Percona

Initial release

2010

2008

2012

2016

2015

Current release

3.1.3, April 2022

3.4.10-2.10, November 2017

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

GPL Version 2

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

C++

Server operating systems

hosted

All OS with a Java VM

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

SQL

Support of SQL

yes

SQL-like query language (GQL)

SQL-like DML and DDL statements

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC
Thrift

JDBC
ODBC
RESTful HTTP API

proprietary protocol using JSON

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++
Java
PHP
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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

using Google App Engine

yes

user defined functions and integration of map-reduce

JavaScript

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

via In-Memory Engine

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 based on Google Cloud Identity and Access Management (IAM)

Access rights for users, groups and roles

Access rights for users and roles

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