DBMS > Apache Impala vs. Datomic vs. Microsoft Azure Cosmos DB vs. MonetDB

System Properties Comparison Apache Impala vs. Datomic vs. Microsoft Azure Cosmos DB vs. MonetDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Globally distributed, horizontally scalable, multi-model database service

A relational database management system that stores data in columns

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store

Spatial DBMS

Document store
Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Website

impala.apache.org

www.datomic.com

azure.microsoft.com/services/cosmos-db

www.monetdb.org

Technical documentation

impala.apache.org/impala-docs.html

docs.datomic.com

learn.microsoft.com/azure/cosmos-db

www.monetdb.org/Documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Cognitect

Microsoft

MonetDB BV

Initial release

2013

2012

2014

2004

Current release

4.1.0, June 2022

1.0.7075, December 2023

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

limited edition free

commercial

Open Source

Mozilla Public License 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, Clojure

Server operating systems

Linux

All OS with a Java VM

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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.

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

SQL 2003 with some extensions

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Transaction Functions

JavaScript

yes, in SQL, C, R

Triggers

By using transaction functions

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

Implicit feature of the cloud service

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

But extensive use of caching in the application peers

yes

Implicit feature of the cloud service

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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