DBMS > Apache Phoenix vs. Fauna vs. Google BigQuery vs. Google Cloud Bigtable vs. IBM Db2 warehouse

System Properties Comparison Apache Phoenix vs. Fauna vs. Google BigQuery vs. Google Cloud Bigtable vs. IBM Db2 warehouse

Editorial information provided by DB-Engines

Name

Description

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

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

Large scale data warehouse service with append-only tables

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Cloud-based data warehousing service

Primary database model

Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store
Wide column store

Relational DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Website

phoenix.apache.org

fauna.com

cloud.google.com/bigquery

cloud.google.com/bigtable

www.ibm.com/products/db2/warehouse

Technical documentation

phoenix.apache.org

docs.fauna.com

cloud.google.com/bigquery/docs

cloud.google.com/bigtable/docs

Developer

Apache Software Foundation

Fauna, Inc.

Google

IBM

Initial release

2014

2010

2015

2014

Current release

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

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

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

Scala

Server operating systems

Linux
Unix
Windows

hosted

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

yes

schema-free

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC

RESTful HTTP API

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

.NET Client API
JDBC
ODBC
OLE DB

Supported programming languages

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

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Python

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

PL/SQL, SQL PL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

consistent hashing

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Atomic single-row operations

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

Identity management, authentication, and 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)

fine grained access rights according to SQL-standard

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