DBMS > Apache Druid vs. atoti vs. Microsoft Azure Cosmos DB vs. Teradata vs. Trafodion

System Properties Comparison Apache Druid vs. atoti vs. Microsoft Azure Cosmos DB vs. Teradata vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

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

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Globally distributed, horizontally scalable, multi-model database service

A hybrid cloud data analytics software platform (Teradata Vantage)

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS
Time Series DBMS

Object oriented DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

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

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

druid.apache.org

atoti.io

azure.microsoft.com/services/cosmos-db

www.teradata.com

trafodion.apache.org

Technical documentation

druid.apache.org/docs/latest/design

docs.atoti.io

learn.microsoft.com/azure/cosmos-db

docs.teradata.com

trafodion.apache.org/documentation.html

Developer

Apache Software Foundation and contributors

ActiveViam

Microsoft

Teradata

Apache Software Foundation, originally developed by HP

Initial release

2012

2014

1984

2014

Current release

29.0.1, April 2024

Teradata Vantage 1.0 MU2, January 2019

2.3.0, February 2019

License

Commercial or Open Source

Open Source

Apache license v2

commercial

free versions available

commercial

Open Source

Apache 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

Java

C++, Java

Server operating systems

Linux
OS X
Unix

hosted

hosted
Linux

Linux

Data scheme

yes

schema-less columns are supported

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.

yes

Secondary indexes

yes

All properties auto-indexed by default

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

yes

SQL

Support of SQL

SQL for querying

Multidimensional Expressions (MDX)

SQL-like query language

yes

SQL 2016 + extensions

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

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

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

ADO.NET
JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

Python

JavaScript

yes

UDFs, stored procedures, table functions in parallel

Java Stored Procedures

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding, horizontal partitioning

Sharding

Implicit feature of the cloud service

Sharding

Hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

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

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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