DBMS > Google BigQuery vs. IBM Db2 Event Store vs. JaguarDB vs. Newts vs. SAP IQ

System Properties Comparison Google BigQuery vs. IBM Db2 Event Store vs. JaguarDB vs. Newts vs. SAP IQ

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Distributed Event Store optimized for Internet of Things use cases

Performant, highly scalable DBMS for AI and IoT applications

Time Series DBMS based on Cassandra

Columnar RDBMS optimized for Big Data analytics

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Key-value store
Vector DBMS

Time Series DBMS

Relational DBMS

column-oriented

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Website

cloud.google.com/bigquery

www.ibm.com/products/db2-event-store

www.jaguardb.com

opennms.github.io/newts

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

Technical documentation

cloud.google.com/bigquery/docs

www.ibm.com/docs/en/db2-event-store

www.jaguardb.com/support.html

github.com/OpenNMS/newts/wiki

help.sap.com/docs/SAP_IQ

Developer

Google

IBM

DataJaguar, Inc.

OpenNMS Group

SAP, formerly Sybase

Initial release

2010

2017

2015

2014

1994

Current release

2.0

3.3 July 2023

16.1 SPS04, April 2019

License

Commercial or Open Source

commercial

free developer edition available

Open Source

GPL V3.0

Open Source

Apache 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

C and C++

C++

the server part. Clients available in other languages

Java

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

HTTP REST
Java API

ADO.NET
JDBC
ODBC
RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Java

C
C#
C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

shared disk or shared nothing architectures with SAP IQ Multiplexer

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Multi-source replication

selectable replication factor

based on Cassandra

SAP/Sybase Replication Server

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

rights management via user accounts

fine grained access rights according to SQL-standard

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