DBMS > Apache IoTDB vs. GridGain vs. Kinetica vs. Microsoft Azure Table Storage vs. Microsoft SQL Server

System Properties Comparison Apache IoTDB vs. GridGain vs. Kinetica vs. Microsoft Azure Table Storage 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

GridGain is an in-memory computing platform, built on Apache Ignite

Fully vectorized database across both GPUs and CPUs

A Wide Column Store for rapid development using massive semi-structured datasets

Microsofts flagship relational DBMS

Primary database model

Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

iotdb.apache.org

www.gridgain.com

www.kinetica.com

azure.microsoft.com/en-us/services/storage/tables

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

Technical documentation

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

www.gridgain.com/docs/index.html

docs.kinetica.com

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

Developer

Apache Software Foundation

GridGain Systems, Inc.

Kinetica

Microsoft

Initial release

2018

2007

2012

1989

Current release

1.1.0, April 2023

GridGain 8.5.1

7.1, August 2021

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

Java, C++, .Net

C, C++

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
OS X
Solaris
Windows

Linux

hosted

Linux
Windows

Data scheme

yes

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

ANSI-99 for query and DML statements, subset of DDL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
Native API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

C++
Java
JavaScript (Node.js)
Python

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

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

Server-side scripts

Stored procedures

yes

yes (compute grid and cache interceptors can be used instead)

user defined functions

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

Triggers

yes

yes (cache interceptors and events)

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Implicit feature of the cloud service

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 cache)

Source-replica replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

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

GPU vRAM or System RAM

yes

User concepts

Access control

yes

Security Hooks for custom implementations

Access rights for users and roles on table level

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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