DBMS > Badger vs. BigObject vs. Kyligence Enterprise vs. Microsoft SQL Server

System Properties Comparison Badger vs. BigObject vs. Kyligence Enterprise vs. Microsoft SQL Server

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Analytic DBMS for real-time computations and queries

A distributed analytics engine for big data, built on top of Apache Kylin

Microsofts flagship relational DBMS

Primary database model

Key-value store

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.46
Rank	#266	Overall
	#124	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

github.com/dgraph-io/badger

bigobject.io

kyligence.io/kyligence-enterprise

www.microsoft.com/en-us/sql-server

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.bigobject.io

learn.microsoft.com/en-US/sql/sql-server

Developer

DGraph Labs

BigObject, Inc.

Kyligence, Inc.

Microsoft

Initial release

2017

2015

2016

1989

Current release

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free community edition available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

Linux
Windows

Data scheme

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

ANSI SQL for queries (using Apache Calcite)

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Supported programming languages

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

Lua

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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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