DBMS > GridGain vs. Hive vs. Microsoft Azure Synapse Analytics vs. MySQL vs. NuoDB

System Properties Comparison GridGain vs. Hive vs. Microsoft Azure Synapse Analytics vs. MySQL vs. NuoDB

Editorial information provided by DB-Engines

Name

Description

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

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

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Widely used open source RDBMS

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.94
Rank	#197	Overall
	#92	Relational DBMS

Website

www.gridgain.com

hive.apache.org

azure.microsoft.com/services/synapse-analytics

www.mysql.com

www.3ds.com/nuodb-distributed-sql-database

Technical documentation

www.gridgain.com/docs/index.html

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

docs.microsoft.com/azure/synapse-analytics

dev.mysql.com/doc

doc.nuodb.com

Developer

GridGain Systems, Inc.

Apache Software Foundation

initially developed by Facebook

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Dassault Systèmes

originally NuoDB, Inc.

Initial release

2007

2012

2016

1995

2013

Current release

GridGain 8.5.1

3.1.3, April 2022

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

limited edition free

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java, C++, .Net

Java

C++

C and C++

C++

Server operating systems

Linux
OS X
Solaris
Windows

All OS with a Java VM

hosted

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Data scheme

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

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

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

APIs and other access methods

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

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C++
Java
PHP
Python

C#
Java
PHP

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

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

yes

user defined functions and integration of map-reduce

Transact SQL

yes

proprietary syntax

Java, SQL

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

data is dynamically stored/cached on the nodes where it is read/written

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

selectable replication factor

yes

Multi-source replication
Source-replica replication

yes

Managed transparently by NuoDB

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

MVCC

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

Temporary table

User concepts

Access control

Security Hooks for custom implementations

Access rights for users, groups and roles

yes

Users with fine-grained authorization concept

no user groups or roles

Standard SQL roles/ privileges, Administrative Users

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