DBMS > ITTIA vs. JaguarDB vs. Snowflake vs. Spark SQL

System Properties Comparison ITTIA vs. JaguarDB vs. Snowflake vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Edge Time Series DBMS with Real-Time Stream Processing

Performant, highly scalable DBMS for AI and IoT applications

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Time Series DBMS

Key-value store
Vector DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.44
Rank	#267	Overall
	#23	Time Series DBMS

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

www.jaguardb.com/support.html

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

ITTIA L.L.C.

DataJaguar, Inc.

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2007

2015

2014

Current release

8.7

3.3 July 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

commercial

Open Source

Apache 2.0

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

Scala

Server operating systems

Linux
Windows

Linux

hosted

Linux
OS X
Windows

Data scheme

Fixed schema

yes

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

yes

Typing

predefined data types such as float or date

Float/Blob/Integer/String/Unicode/Date/Time/Timestamp/Interval

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
RESTful HTTP API
WebSocket

JDBC
ODBC

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

C
C++
JavaScript (Node.js)
Python

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

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

Database file passwords

rights management via user accounts

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

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