DBMS > Derby vs. Google Cloud Datastore vs. JaguarDB vs. Netezza vs. Snowflake

System Properties Comparison Derby vs. Google Cloud Datastore vs. JaguarDB vs. Netezza vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Performant, highly scalable DBMS for AI and IoT applications

Data warehouse and analytics appliance part of IBM PureSystems

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

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Trend Chart

Score	4.69
Rank	#72	Overall
	#39	Relational DBMS

Score	4.35
Rank	#79	Overall
	#11	Document stores

Score	0.02
Rank	#334	Overall
	#54	Key-value stores

Score	19.20
Rank	#35	Overall
	#21	Relational DBMS

Score	26.46
Rank	#29	Overall
	#16	Relational DBMS

Website

db.apache.org/derby

cloud.google.com/datastore

www.datajaguar.com

www.ibm.com/products/netezza

www.snowflake.com

Technical documentation

db.apache.org/derby/manuals/index.html

cloud.google.com/datastore/docs

www.datajaguar.com/support.php

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Google

DataJaguar, Inc.

IBM

Snowflake Computing Inc.

Initial release

1997

2008

2015

2000

2014

Current release

10.15.2.0, March 2020

2.9.4 September 2018

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

GPL V3.0

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

Java

C++

the server part. Clients available in other languages

Server operating systems

All OS with a Java VM

hosted

Linux

included in appliance

hosted

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

yes, details here

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

yes

SQL-like query language (GQL)

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

APIs and other access methods

JDBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC

JDBC
ODBC
OLE DB

CLI Client
JDBC
ODBC

Supported programming languages

Java

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

C
C++
Fortran
Java
Lua
Perl
Python
R

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Java Stored Procedures

using Google App Engine

yes

user defined functions

Triggers

yes

Callbacks using the Google Apps Engine

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication using Paxos

Multi-source replication

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

rights management via user accounts

Users with fine-grained authorization concept

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

SQL support, group by, order by, join
» more

Competitive advantages

Extremely easy to scale-out (in seconds) 100,000 times faster
» more

Typical application scenarios

IoT data, time series data, AI data. Any type of numerical data.
» more

Key customers

Quantea, 2PiDay, Synqy, SinoLogistics, IIVI
» more

Market metrics

Advertising awareness, installed base, brand image, market share
» more

Licensing and pricing models

Free-trial license, per-node license, and subscription license.
» more

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