DBMS > Couchbase vs. IBM Db2 vs. Microsoft Azure Data Explorer vs. NSDb vs. SAP SQL Anywhere

System Properties Comparison Couchbase vs. IBM Db2 vs. Microsoft Azure Data Explorer vs. NSDb vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Common in IBM host environments, 2 different versions for host and Windows/Linux

Fully managed big data interactive analytics platform

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

Primary database model

Document store

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

column oriented

Time Series DBMS

Relational DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	125.90
Rank	#9	Overall
	#6	Relational DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Website

www.couchbase.com

www.ibm.com/products/db2

azure.microsoft.com/services/data-explorer

nsdb.io

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

Technical documentation

docs.couchbase.com

www.ibm.com/docs/en/db2

docs.microsoft.com/en-us/azure/data-explorer

nsdb.io/Architecture

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Couchbase, Inc.

IBM

Microsoft

SAP

formerly Sybase

Initial release

2011

1983

host version

2019

2017

1992

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

12.1, October 2016

cloud service with continuous releases

17, July 2015

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

free version is available

commercial

Open Source

Apache Version 2.0

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++, Go and Erlang

C and C++

Java, Scala

Server operating systems

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows
z/OS

hosted

Linux
macOS

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

yes

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

Kusto Query Language (KQL), SQL subset

SQL-like query language

yes

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

gRPC
HTTP REST
WebSocket

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

Java
Scala

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

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

Yes, possible languages: KQL, Python, R

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

Triggers

yes

via the TAP protocol

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

only with Windows/Unix/Linux Version

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

yes

with separate tools (MQ, InfoSphere)

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency
Immediate Consistency

Eventual Consistency

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

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

In-memory capabilities

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

yes

Ephemeral buckets

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

fine grained access rights according to SQL-standard

Azure Active Directory Authentication

fine grained access rights according to SQL-standard

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