DBMS > EXASOL vs. JaguarDB vs. Snowflake vs. Vertica

System Properties Comparison EXASOL vs. JaguarDB vs. Snowflake vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Performant, highly scalable DBMS for AI and IoT applications

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

Columnar relational DBMS designed to handle modern analytic workloads, enabling fast query performance

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

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

Score	2.83
Rank	#87	Overall
	#44	Relational DBMS

Score	0.00
Rank	#338	Overall
	#60	Key-value stores

Score	2.18
Rank	#106	Overall
	#51	Relational DBMS

Score	22.17
Rank	#30	Overall
	#18	Relational DBMS

Website

www.exasol.com/en/products

www.datajaguar.com

www.snowflake.com

www.vertica.com

Technical documentation

www.exasol.com/en/community/resources

www.datajaguar.com/support.php

docs.snowflake.net/manuals/index.html

www.vertica.com/documentation/vertica

Developer

Exasol

DataJaguar, Inc.

Snowflake Computing Inc.

Vertica / Micro Focus info

prior to that Hewlett Packard

Initial release

2000

2015

2014

2005

Current release

2.9.4 September 2018

Vertica Analytics Platform 9.2.x, March 2019

License

Commercial or Open Source

commercial

Open Source info

GPL V3.0

commercial

commercial info

Limited community edition free

Cloud-based only info

Only available as a cloud service

yes

available on Amazon AWS and Microsoft Azure

DBaaS offerings (sponsored links) info

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

Server operating systems

Linux

hosted

Linux

Data scheme

yes

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

yes

Unstructured data can be stored in specific Flex-Tables

Typing

predefined data types such as float or date

yes

XML support info

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

The concept of 'projections' can be used instead of indexes.

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented info

but no views, foreign keys, triggers

yes

nearly full SQL99 without support for indexes and triggers

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC

CLI Client
JDBC
ODBC

ADO.NET
JDBC
Kafka
ODBC
Proprietary protocol
RESTful HTTP API

Supported programming languages

Java
Lua
Python
R

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

JavaScript (Node.js)
Python

C++
Java
Perl
Python
R

Server-side scripts info

Stored procedures

user defined functions

yes

Triggers

yes

similar concept for controling cloud resources

Partitioning methods info

Methods for storing different data on different nodes

Sharding

yes

Sharding

Segmentation, Projection Segmentation, Hash Segmentation

Replication methods info

Methods for redundantly storing data on multiple nodes

Master-master replication

yes

Master-master replication info

Cluster level and Object level replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

with Vertica Connector for Hadoop

Consistency concepts info

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys info

Referential integrity

yes

Transaction concepts info

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

ACID

Concurrency info

Support for concurrent manipulation of data

yes

Durability info

Support for making data persistent

yes

In-memory capabilities info

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts info

Access control

Access rights for users, groups and roles according to SQL-standard

rights management via user accounts

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

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

SQL support, group by, order by, join
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Columnar storage, MPP, Hybrid storage model, Aggressive data compression, High availability,...
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Competitive advantages

Extremely easy to scale-out (in seconds) 100,000 times faster
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Performance at exabyte scale, Deploy anywhere, Open source innovation, Analysis in...
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Typical application scenarios

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

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customers

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

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Market metrics

Advertising awareness, installed base, brand image, market share
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Licensing and pricing models

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

Cost-based models and subscription-based models are available
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