DBMS > FatDB vs. Snowflake vs. Splunk vs. Vertica

System Properties Comparison FatDB vs. Snowflake vs. Splunk vs. Vertica

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

Analytics Platform for Big Data

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store
Key-value store

Relational DBMS

Search engine

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	93.02
Rank	#13	Overall
	#2	Search engines

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

www.snowflake.com

www.splunk.com

www.vertica.com

Technical documentation

docs.snowflake.net/manuals/index.html

docs.splunk.com/Documentation/Splunk

vertica.com/documentation

Developer

FatCloud

Snowflake Computing Inc.

Splunk Inc.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2014

2003

2005

Current release

12.0.3, January 2023

License

Commercial or Open Source

commercial

Limited free edition and free developer edition available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

Windows

hosted

Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

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

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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.

yes

Secondary indexes

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

Via inetgration in SQL Server

yes

Splunk Search Processing Language for search commands

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

CLI Client
JDBC
ODBC

HTTP REST

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

JavaScript (Node.js)
Python

C#
Java
JavaScript
PHP
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

via applications

user defined functions

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

via applications

similar concept for controling cloud resources

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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

Can implement custom security layer via applications

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

Access rights for users and roles

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

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