DBMS > Apache Phoenix vs. EXASOL vs. Google BigQuery vs. GridGain vs. Hive

System Properties Comparison Apache Phoenix vs. EXASOL vs. Google BigQuery vs. GridGain vs. Hive

Editorial information provided by DB-Engines

Name

Description

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

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Large scale data warehouse service with append-only tables

GridGain is an in-memory computing platform, built on Apache Ignite

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

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Trend Chart

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Website

phoenix.apache.org

www.exasol.com

cloud.google.com/bigquery

www.gridgain.com

hive.apache.org

Technical documentation

phoenix.apache.org

www.exasol.com/resources

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

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

Developer

Apache Software Foundation

Exasol

Google

GridGain Systems, Inc.

Apache Software Foundation

initially developed by Facebook

Initial release

2014

2000

2010

2007

2012

Current release

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

GridGain 8.5.1

3.1.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

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

Java, C++, .Net

Java

Server operating systems

Linux
Unix
Windows

hosted

Linux
OS X
Solaris
Windows

All OS with a Java VM

Data scheme

yes

late-bound, schema-on-read capabilities

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

SQL-like DML and DDL statements

APIs and other access methods

JDBC

.Net
JDBC
ODBC
WebSocket

RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC
Thrift

Supported programming languages

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

Java
Lua
Python
R

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes (compute grid and cache interceptors can be used instead)

yes

user defined functions and integration of map-reduce

Triggers

yes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes (replicated cache)

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Hadoop integration

yes (compute grid and hadoop accelerator)

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual 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

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

Access rights for users, groups and roles according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

Access rights for users, groups and roles

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