DBMS > Databricks vs. FatDB vs. HBase vs. ReductStore vs. Sequoiadb

System Properties Comparison Databricks vs. FatDB vs. HBase vs. ReductStore vs. Sequoiadb

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

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

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

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

NewSQL database with distributed OLTP and SQL

Primary database model

Document store
Relational DBMS

Document store
Key-value store

Wide column store

Time Series DBMS

Document store
Relational DBMS

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

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Website

www.databricks.com

hbase.apache.org

github.com/reductstore
www.reduct.store

www.sequoiadb.com

Technical documentation

docs.databricks.com

hbase.apache.org/book.html

www.reduct.store/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Databricks

FatCloud

Apache Software Foundation

Apache top-level project, originally developed by Powerset

ReductStore LLC

Sequoiadb Ltd.

Initial release

2013

2012

2008

2023

2013

Current release

2.3.4, January 2021

1.9, March 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Business Source License 1.1

Open Source

Server: AGPL; Client: Apache V2

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++, Rust

C++

Server operating systems

hosted

Windows

Linux
Unix
Windows

using Cygwin

Docker
Linux
macOS
Windows

Linux

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

schema-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

oid, date, timestamp, binary, regex

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

with Databricks SQL

Via inetgration in SQL Server

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

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

Java API
RESTful HTTP API
Thrift

HTTP API

proprietary protocol using JSON

Supported programming languages

Python
R
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
JavaScript (Node.js)
Python
Rust

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

via applications

yes

Coprocessors in Java

JavaScript

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

Document is locked during a transaction

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

Can implement custom security layer via applications

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

simple password-based access control

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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