DBMS > Apache Phoenix vs. Google BigQuery vs. Oracle Coherence vs. Warp 10

System Properties Comparison Apache Phoenix vs. Google BigQuery vs. Oracle Coherence vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Large scale data warehouse service with append-only tables

Oracles in-memory data grid solution

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

Primary database model

Relational DBMS

Key-value store

Time Series DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

phoenix.apache.org

cloud.google.com/bigquery

www.oracle.com/java/coherence

www.warp10.io

Technical documentation

phoenix.apache.org

cloud.google.com/bigquery/docs

docs.oracle.com/en/middleware/standalone/coherence

www.warp10.io/content/02_Getting_started

Developer

Apache Software Foundation

Google

Oracle

SenX

Initial release

2014

2010

2007

2015

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

14.1, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache License 2.0

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

Server operating systems

Linux
Unix
Windows

hosted

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

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

SQL

Support of SQL

yes

APIs and other access methods

JDBC

RESTful HTTP/JSON API

JCache
JPA
RESTful HTTP API

HTTP API
Jupyter
WebSocket

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

WarpScript

Triggers

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes, with selectable consistency level

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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

Google Cloud Identity & Access Management (IAM)

authentification to access the cache via certificates or http basic authentication

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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