DBMS > CockroachDB vs. Google BigQuery vs. JaguarDB vs. Newts vs. SAP IQ

System Properties Comparison CockroachDB vs. Google BigQuery vs. JaguarDB vs. Newts vs. SAP IQ

Editorial information provided by DB-Engines

Name

Description

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

Large scale data warehouse service with append-only tables

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

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	6.15
Rank	#55	Overall
	#33	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational 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

www.cockroachlabs.com

cloud.google.com/bigquery

www.jaguardb.com

opennms.github.io/newts

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

Technical documentation

www.cockroachlabs.com/docs

cloud.google.com/bigquery/docs

www.jaguardb.com/support.html

github.com/OpenNMS/newts/wiki

help.sap.com/docs/SAP_IQ

Developer

Cockroach Labs

Google

DataJaguar, Inc.

OpenNMS Group

SAP, formerly Sybase

Initial release

2015

2010

2015

2014

1994

Current release

23.1.1, May 2023

3.3 July 2023

16.1 SPS04, April 2019

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

commercial

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

the server part. Clients available in other languages

Java

Server operating systems

Linux
macOS
Windows

hosted

Linux

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

dynamic schema

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, wire compatible with PostgreSQL

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

APIs and other access methods

JDBC

RESTful HTTP/JSON API

JDBC
ODBC

HTTP REST
Java API

ADO.NET
JDBC
ODBC
RESTful HTTP API

Supported programming languages

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

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

Multi-source replication using RAFT

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

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.

User concepts

Access control

Role-based access control

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

Google Cloud Identity & Access Management (IAM)

rights management via user accounts

fine grained access rights according to SQL-standard

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