DBMS > atoti vs. Bangdb vs. Microsoft Azure Synapse Analytics vs. Oracle Berkeley DB

System Properties Comparison atoti vs. Bangdb vs. Microsoft Azure Synapse Analytics vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Widely used in-process key-value store

Primary database model

Object oriented DBMS

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Spatial DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

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

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Website

atoti.io

bangdb.com

azure.microsoft.com/services/synapse-analytics

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

docs.atoti.io

docs.bangdb.com

docs.microsoft.com/azure/synapse-analytics

docs.oracle.com/cd/E17076_05/html/index.html

Developer

ActiveViam

Sachin Sinha, BangDB

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2012

2016

1994

Current release

BangDB 2.0, October 2021

18.1.40, May 2020

License

Commercial or Open Source

commercial

free versions available

Open Source

BSD 3

commercial

Open Source

commercial license available

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, C++

C++

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

schema-free

yes

schema-free

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

only with the Berkeley DB XML edition

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL like support with command line tool

yes

SQL interfaced based on SQLite is available

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

C#
Java
PHP

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

Python

Transact SQL

Triggers

yes, Notifications (with Streaming only)

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

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

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Source-replica replication

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

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

yes

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