DBMS > GridGain vs. Microsoft Azure Synapse Analytics vs. SWC-DB vs. Warp 10

System Properties Comparison GridGain vs. Microsoft Azure Synapse Analytics vs. SWC-DB vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

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

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

A high performance, scalable Wide Column DBMS

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Wide column store

Time Series DBMS

Secondary database models

Time Series DBMS

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

Score	1.53
Rank	#155	Overall
	#26	Key-value stores
	#73	Relational DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Score	0.01
Rank	#387	Overall
	#13	Wide column stores

Score	0.08
Rank	#358	Overall
	#35	Time Series DBMS

Website

www.gridgain.com

azure.microsoft.com/services/synapse-analytics

github.com/kashirin-alex/swc-db
www.swcdb.org

www.warp10.io

Technical documentation

www.gridgain.com/docs/index.html

docs.microsoft.com/azure/synapse-analytics

www.warp10.io/content/02_Getting_started

Developer

GridGain Systems, Inc.

Microsoft

Alex Kashirin

SenX

Initial release

2007

2016

2020

2015

Current release

GridGain 8.5.1

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

GPL V3

Open Source

Apache License 2.0

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

Java, C++, .Net

C++

Java

Server operating systems

Linux
OS X
Solaris
Windows

hosted

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

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

yes

SQL-like query language

APIs and other access methods

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

ADO.NET
JDBC
ODBC

Proprietary protocol
Thrift

HTTP API
Jupyter
WebSocket

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C#
Java
PHP

C++

Server-side scripts

Stored procedures

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

Transact SQL

yes

WarpScript

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

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

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

Security Hooks for custom implementations

yes

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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