DBMS > Apache Impala vs. Google BigQuery vs. InfluxDB vs. InterSystems IRIS

System Properties Comparison Apache Impala vs. Google BigQuery vs. InfluxDB vs. InterSystems IRIS

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Large scale data warehouse service with append-only tables

DBMS for storing time series, events and metrics

A containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalability

Primary database model

Relational DBMS

Time Series DBMS

Document store
Key-value store
Object oriented DBMS
Relational DBMS

Secondary database models

Document store

Spatial DBMS

with GEO package

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	4.05
Rank	#83	Overall
	#14	Document stores
	#10	Key-value stores
	#1	Object oriented DBMS
	#45	Relational DBMS

Website

impala.apache.org

cloud.google.com/bigquery

www.influxdata.com/products/influxdb-overview

www.intersystems.com/products/intersystems-iris

Technical documentation

impala.apache.org/impala-docs.html

cloud.google.com/bigquery/docs

docs.influxdata.com/influxdb

docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

InterSystems

Initial release

2013

2010

2013

2018

Current release

4.1.0, June 2022

2.7.6, April 2024

2023.3, June 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

MIT-License; commercial enterprise version available

commercial

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

Linux
OS X

through Homebrew

AIX
Linux
macOS
Ubuntu
Windows

Data scheme

yes

schema-free

depending on used data model

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

HTTP API
JSON over UDP

JDBC
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
C++
Java
JavaScript (Node.js)
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

Sharding

in enterprise version only

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

in enterprise version only

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

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

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

Depending on used storage engine

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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

Google Cloud Identity & Access Management (IAM)

simple rights management via user accounts

yes

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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