DBMS > Databricks vs. HEAVY.AI vs. Hive vs. Microsoft SQL Server vs. SiteWhere

System Properties Comparison Databricks vs. HEAVY.AI vs. Hive vs. Microsoft SQL Server vs. SiteWhere

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.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Microsofts flagship relational DBMS

M2M integration platform for persisting/querying time series data

Primary database model

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial 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	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

www.databricks.com

github.com/heavyai/heavydb
www.heavy.ai

hive.apache.org

www.microsoft.com/en-us/sql-server

github.com/sitewhere/sitewhere

Technical documentation

docs.databricks.com

docs.heavy.ai

cwiki.apache.org/confluence/display/Hive/Home

learn.microsoft.com/en-US/sql/sql-server

sitewhere1.sitewhere.io/index.html

Developer

Databricks

HEAVY.AI, Inc.

Apache Software Foundation

initially developed by Facebook

Microsoft

SiteWhere

Initial release

2013

2016

2012

1989

2010

Current release

5.10, January 2022

3.1.3, April 2022

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++ and CUDA

Java

C++

Java

Server operating systems

hosted

Linux

All OS with a Java VM

Linux
Windows

Linux
OS X
Windows

Data scheme

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

yes

predefined scheme

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

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST

Supported programming languages

Python
R
Scala

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
PHP
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

user defined functions and integration of map-reduce

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

selectable replication factor

yes, but depending on the SQL-Server Edition

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

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

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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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