DBMS > Databricks vs. Microsoft Azure Synapse Analytics vs. VoltDB

System Properties Comparison Databricks vs. Microsoft Azure Synapse Analytics vs. VoltDB

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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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Document store
Relational DBMS

Relational 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	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.databricks.com

azure.microsoft.com/services/synapse-analytics

www.voltdb.com

Technical documentation

docs.databricks.com

docs.microsoft.com/azure/synapse-analytics

docs.voltdb.com

Developer

Databricks

Microsoft

VoltDB Inc.

Initial release

2013

2016

2010

Current release

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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++

Java, C++

Server operating systems

hosted

Linux
OS X

for development

Data scheme

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

with Databricks SQL

yes

only a subset of SQL 99

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Python
R
Scala

C#
Java
PHP

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

Transact SQL

Java

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
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

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

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

User concepts

Access control

yes

Users and roles with access to stored procedures

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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Recent citations in the news

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