DBMS > BigObject vs. etcd vs. EventStoreDB vs. Microsoft Azure SQL Database

System Properties Comparison BigObject vs. etcd vs. EventStoreDB vs. Microsoft Azure SQL Database

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

A distributed reliable key-value store

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Database as a Service offering with high compatibility to Microsoft SQL Server

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Event Store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Website

bigobject.io

etcd.io
github.com/etcd-io/etcd

www.eventstore.com

azure.microsoft.com/en-us/products/azure-sql/database

Technical documentation

docs.bigobject.io

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

developers.eventstore.com

docs.microsoft.com/en-us/azure/azure-sql

Developer

BigObject, Inc.

Event Store Limited

Microsoft

Initial release

2015

2012

2010

Current release

3.4, August 2019

21.2, February 2021

V12

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2.0

Open Source

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++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
Windows

experimental

Linux
Windows

hosted

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

gRPC
JSON over HTTP

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

Lua

Transact SQL

Triggers

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

yes, with always 3 replicas available

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

fine grained access rights according to SQL-standard

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More resources

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