DBMS > Bangdb vs. Databricks vs. HBase vs. Sequoiadb vs. Stardog

System Properties Comparison Bangdb vs. Databricks vs. HBase vs. Sequoiadb vs. Stardog

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

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.

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

NewSQL database with distributed OLTP and SQL

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Document store
Graph DBMS
Time Series DBMS

Document store
Relational DBMS

Wide column store

Document store
Relational DBMS

Graph DBMS
RDF store

Secondary database models

Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

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

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

Technical documentation

docs.bangdb.com

docs.databricks.com

hbase.apache.org/book.html

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

docs.stardog.com

Developer

Sachin Sinha, BangDB

Databricks

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Sequoiadb Ltd.

Stardog-Union

Initial release

2012

2013

2008

2013

2010

Current release

BangDB 2.0, October 2021

2.3.4, January 2021

7.3.0, May 2020

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

Apache version 2

Open Source

Server: AGPL; Client: Apache V2

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

C, C++

Java

C++

Java

Server operating systems

Linux

hosted

Linux
Unix
Windows

using Cygwin

Linux

Linux
macOS
Windows

Data scheme

schema-free

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

schema-free, schema definition possible

schema-free

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

options to bring your own types, AVRO

yes

oid, date, timestamp, binary, regex

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

Import/export of XML data possible

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL like support with command line tool

with Databricks SQL

SQL-like query language

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

proprietary protocol using JSON

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

C
C#
C++
Java
Python

Python
R
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
C++
Java
PHP
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Coprocessors in Java

JavaScript

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes, Notifications (with Streaming only)

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

yes

Multi-source replication
Source-replica replication

Source-replica replication

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

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

simple password-based access control

Access rights for users and roles

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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