DBMS > Apache Impala vs. EXASOL vs. Google BigQuery vs. OpenQM

System Properties Comparison Apache Impala vs. EXASOL vs. Google BigQuery vs. OpenQM

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Large scale data warehouse service with append-only tables

QpenQM is a high-performance, self-tuning, multi-value DBMS

Primary database model

Relational DBMS

Multivalue DBMS

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.34
Rank	#284	Overall
	#10	Multivalue DBMS

Website

impala.apache.org

www.exasol.com

cloud.google.com/bigquery

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

Technical documentation

impala.apache.org/impala-docs.html

www.exasol.com/resources

cloud.google.com/bigquery/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Exasol

Google

Rocket Software, originally Martin Phillips

Initial release

2013

2000

2010

1993

Current release

4.1.0, June 2022

3.4-12

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GPLv2, extended commercial license available

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

C++

Server operating systems

Linux

hosted

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Data scheme

yes

with some exceptions

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

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

.Net
JDBC
ODBC
WebSocket

RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

Java
Lua
Python
R

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Basic
C
Java
Objective C
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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 rights for users, groups and roles

based on Apache Sentry and Kerberos

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)

Access rights can be defined down to the item level

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