DBMS > Databricks vs. Microsoft Azure Synapse Analytics vs. UniData,UniVerse

System Properties Comparison Databricks vs. Microsoft Azure Synapse Analytics vs. UniData,UniVerse

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Editorial information provided by DB-Engines

Name

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.

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

MultiValue database and application server with SQL mapping layer and meta database capabilities

Primary database model

Document store
Relational DBMS

Relational DBMS

Multivalue 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	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	3.16
Rank	#97	Overall
	#2	Multivalue DBMS

Website

www.databricks.com

azure.microsoft.com/services/synapse-analytics

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

Technical documentation

docs.databricks.com

docs.microsoft.com/azure/synapse-analytics

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

Developer

Databricks

Microsoft

Rocket Software

Initial release

2013

2016

1985

License

Commercial or Open Source

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++

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

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

yes

schema-free

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

with Databricks SQL

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

Python
R
Scala

C#
Java
PHP

.Net
Basic

U2 Basic
C
Java

Server-side scripts

Stored procedures

user defined functions and aggregates

Transact SQL

yes

Triggers

yes

U2 Basic

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

ACID

configurable

Concurrency

Support for concurrent manipulation of data

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.

User concepts

Access control

yes

Access rights according to SQL-standard and operating system based

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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