DBMS > Apache IoTDB vs. Databricks vs. Hazelcast vs. IBM Cloudant vs. Microsoft SQL Server

System Properties Comparison Apache IoTDB vs. Databricks vs. Hazelcast vs. IBM Cloudant vs. Microsoft SQL Server

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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 widely adopted in-memory data grid

Database as a Service offering based on Apache CouchDB

Microsofts flagship relational DBMS

Primary database model

Time Series DBMS

Document store
Relational DBMS

Key-value store

Document store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Graph DBMS
Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

iotdb.apache.org

www.databricks.com

hazelcast.com

www.ibm.com/products/cloudant

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

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.databricks.com

hazelcast.org/imdg/docs

cloud.ibm.com/docs/Cloudant

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

Developer

Apache Software Foundation

Databricks

Hazelcast

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

Initial release

2018

2013

2008

2010

1989

Current release

1.1.0, April 2023

5.3.6, November 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

Erlang

C++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

All OS with a Java VM

hosted

Linux
Windows

Data scheme

yes

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

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

with Databricks SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

JDBC
ODBC
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

RESTful HTTP/JSON API

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Python
R
Scala

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions and aggregates

yes

Event Listeners, Executor Services

View functions (Map-Reduce) in JavaScript

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

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

Replicated Map

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

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

yes

Role-based 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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