DBMS > Apache Pinot vs. Cubrid vs. Databricks vs. Microsoft Azure Synapse Analytics

System Properties Comparison Apache Pinot vs. Cubrid vs. Databricks vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

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

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

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

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

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

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Website

pinot.apache.org

cubrid.com (korean)
cubrid.org (english)

www.databricks.com

azure.microsoft.com/services/synapse-analytics

Technical documentation

docs.pinot.apache.org

cubrid.org/manuals

docs.databricks.com

docs.microsoft.com/azure/synapse-analytics

Developer

Apache Software Foundation and contributors

CUBRID Corporation, CUBRID Foundation

Databricks

Microsoft

Initial release

2015

2008

2013

2016

Current release

1.0.0, September 2023

11.0, January 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

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

Java

C, C++, Java

C++

Server operating systems

All OS with a Java JDK11 or higher

Linux
Windows

hosted

Data scheme

yes

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

yes

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

with Databricks SQL

yes

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

Go
Java
Python

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

Python
R
Scala

C#
Java
PHP

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions and aggregates

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

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

yes

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

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

fine grained access rights according to SQL-standard

yes

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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