DBMS > Apache Phoenix vs. Google BigQuery vs. GridGain vs. IBM Db2 warehouse vs. SQream DB

System Properties Comparison Apache Phoenix vs. Google BigQuery vs. GridGain vs. IBM Db2 warehouse vs. SQream DB

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

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

Cloud-based data warehousing service

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	1.37
Rank	#160	Overall
	#74	Relational DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

phoenix.apache.org

cloud.google.com/bigquery

www.gridgain.com

www.ibm.com/products/db2/warehouse

sqream.com

Technical documentation

phoenix.apache.org

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

docs.sqream.com

Developer

Apache Software Foundation

Google

GridGain Systems, Inc.

IBM

SQream Technologies

Initial release

2014

2010

2007

2014

2017

Current release

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

GridGain 8.5.1

2022.1.6, December 2022

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

Java, C++, .Net

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux
Unix
Windows

hosted

Linux
OS X
Solaris
Windows

hosted

Linux

Data scheme

yes

late-bound, schema-on-read capabilities

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

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

yes

APIs and other access methods

JDBC

RESTful HTTP/JSON API

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

.NET Client API
JDBC
ODBC
OLE DB

.Net
JDBC
ODBC

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

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

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

PL/SQL, SQL PL

user defined functions in Python

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes (replicated cache)

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

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

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

Google Cloud Identity & Access Management (IAM)

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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