DBMS > Apache Druid vs. IBM Db2 Event Store vs. Microsoft SQL Server vs. Oracle NoSQL

System Properties Comparison Apache Druid vs. IBM Db2 Event Store vs. Microsoft SQL Server vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Distributed Event Store optimized for Internet of Things use cases

Microsofts flagship relational DBMS

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Relational DBMS
Time Series DBMS

Event Store
Time Series DBMS

Relational DBMS

Document store
Key-value 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	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	0.23
Rank	#316	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	2.96
Rank	#103	Overall
	#18	Document stores
	#17	Key-value stores
	#52	Relational DBMS

Website

druid.apache.org

www.ibm.com/products/db2-event-store

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

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

druid.apache.org/docs/latest/design

www.ibm.com/docs/en/db2-event-store

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

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Apache Software Foundation and contributors

IBM

Microsoft

Oracle

Initial release

2012

2017

1989

2011

Current release

29.0.1, April 2024

2.0

SQL Server 2022, November 2022

23.3, December 2023

License

Commercial or Open Source

Open Source

Apache license v2

commercial

free developer edition available

commercial

restricted free version is available

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

Java

C and C++

C++

Java

Server operating systems

Linux
OS X
Unix

Linux

Linux, macOS, Windows for the developer addition

Linux
Windows

Linux
Solaris SPARC/x86

Data scheme

yes

schema-less columns are supported

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

optional

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 for querying

yes

through the embedded Spark runtime

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

RESTful HTTP API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

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

C
C#
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

Active-active shard replication

yes, but depending on the SQL-Server Edition

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

In-memory capabilities

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

yes

off heap cache

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

fine grained access rights according to SQL-standard

Access rights for users and roles

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