DBMS > Databricks vs. IBM Cloudant vs. IBM Db2 warehouse vs. Yanza

System Properties Comparison Databricks vs. IBM Cloudant vs. IBM Db2 warehouse vs. Yanza

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Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Database as a Service offering based on Apache CouchDB

Cloud-based data warehousing service

Time Series DBMS for IoT Applications

Primary database model

Document store
Relational DBMS

Document store

Relational DBMS

Time Series DBMS

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

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Website

www.databricks.com

www.ibm.com/products/cloudant

www.ibm.com/products/db2/warehouse

yanza.com

Technical documentation

docs.databricks.com

cloud.ibm.com/docs/Cloudant

Developer

Databricks

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

IBM

Yanza

Initial release

2013

2010

2014

2015

License

Commercial or Open Source

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Erlang

Server operating systems

hosted

Windows

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

schema-free

yes

schema-free

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

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
OLE DB

HTTP API

Supported programming languages

Python
R
Scala

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions and aggregates

View functions (Map-Reduce) in JavaScript

PL/SQL, SQL PL

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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