DBMS > Bangdb vs. Databricks vs. SAP SQL Anywhere

System Properties Comparison Bangdb vs. Databricks vs. SAP SQL Anywhere

Please select another system to include it in the comparison.

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.

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Document store
Graph DBMS
Time Series DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.08
Rank	#344	Overall
	#48	Document stores
	#35	Graph DBMS
	#32	Time Series DBMS

Score	87.68
Rank	#13	Overall
	#2	Document stores
	#9	Relational DBMS

Score	3.18
Rank	#82	Overall
	#45	Relational DBMS

Website

bangdb.com

www.databricks.com

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

docs.bangdb.com

docs.databricks.com

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Sachin Sinha, BangDB

Databricks

SAP

formerly Sybase

Initial release

2012

2013

1992

Current release

BangDB 2.0, October 2021

17, July 2015

License

Commercial or Open Source

Open Source

BSD 3

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

C, C++

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

schema-free

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

yes

Typing

predefined data types such as float or date

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

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

with Databricks SQL

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

Python
R
Scala

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

yes, in C/C++, Java, .Net or Perl

Triggers

yes, Notifications (with Streaming only)

yes

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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)

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2023
2 January 2024, Matthias Gelbmann, Paul Andlinger

show all

Recent citations in the news

Exclusive: Databricks nears record $9.5 billion VC raise, eyes extra $4.5 billion debt
13 December 2024, Reuters

Databricks is on track to raise a record $9.5+ billion round at $60B valuation
13 December 2024, TechCrunch

Tech Start-Up Databricks Seeks $2.5 Billion Private Debt Financing
13 December 2024, Yahoo Finance

Kleiner Perkins, Founders Fund, SoftBank to Invest in Databricks’ $7 Billion Fundraising
12 December 2024, The Information

They Teamed Up On Databricks And Perplexity. Now They're Launching Their Own $150 Million Fund
10 December 2024, Forbes

provided by Google News

SAP Products & Services Data Portfolio
2 July 2021, Datamation

Rimini Street expands support beyond SAP and Oracle
20 October 2015, InsideSAP

Rimini Street to offer database, security services
7 June 2017, ZDNet

Securing SAP with AWS Network Firewall: Part 2 – Managed Rules
17 July 2023, AWS Blog

SAP vulnerabilities Let Attacker Inject OS Commands—Patch Now!
11 July 2023, CybersecurityNews

provided by Google News

Share this page