DBMS > Faircom DB vs. HBase vs. Qdrant vs. Snowflake

System Properties Comparison Faircom DB vs. HBase vs. Qdrant vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Wide-column store based on Apache Hadoop and on concepts of BigTable

A high-performance vector database with neural network or semantic-based matching

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

Primary database model

Key-value store
Relational DBMS

Wide column store

Vector DBMS

Relational DBMS

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

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.faircom.com/products/faircom-db

hbase.apache.org

github.com/qdrant/qdrant
qdrant.tech

www.snowflake.com

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

hbase.apache.org/book.html

qdrant.tech/documentation

docs.snowflake.net/manuals/index.html

Developer

FairCom Corporation

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Qdrant

Snowflake Computing Inc.

Initial release

1979

2008

2021

2014

Current release

V12, November 2020

2.3.4, January 2021

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache version 2

Open Source

Apache Version 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

ANSI C, C++

Java

Rust

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
Unix
Windows

using Cygwin

Docker
Linux
macOS
Windows

hosted

Data scheme

schema free, schema optional, schema required, partial schema,

schema-free, schema definition possible

schema-free

yes

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

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

options to bring your own types, AVRO

Numbers, Strings, Geo, Boolean

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

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Java API
RESTful HTTP API
Thrift

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

CLI Client
JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Coprocessors in Java

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Multi-source replication
Source-replica replication

Collection-level replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency or Eventual Consistency

Eventual Consistency, tunable consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

Single row ACID (across millions of columns)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

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 with additional protections for files

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Key-based authentication

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

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