DBMS > GridGain vs. HBase vs. Microsoft Azure AI Search vs. SAP IQ vs. Snowflake

System Properties Comparison GridGain vs. HBase vs. Microsoft Azure AI Search vs. SAP IQ vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

Search-as-a-service for web and mobile app development

Columnar RDBMS optimized for Big Data analytics

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Key-value store
Relational DBMS

column-oriented

Secondary database models

Vector DBMS

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

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.gridgain.com

hbase.apache.org

azure.microsoft.com/en-us/services/search

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

www.snowflake.com

Technical documentation

www.gridgain.com/docs/index.html

hbase.apache.org/book.html

learn.microsoft.com/en-us/azure/search

help.sap.com/docs/SAP_IQ

docs.snowflake.net/manuals/index.html

Developer

GridGain Systems, Inc.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

SAP, formerly Sybase

Snowflake Computing Inc.

Initial release

2007

2008

2015

1994

2014

Current release

GridGain 8.5.1

2.3.4, January 2021

16.1 SPS04, April 2019

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Java

Server operating systems

Linux
OS X
Solaris
Windows

Linux
Unix
Windows

using Cygwin

hosted

AIX
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

yes

schema-free, schema definition possible

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

RESTful HTTP API

ADO.NET
JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

C#
Java
JavaScript
Python

C
C#
C++
Java
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

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

yes

Coprocessors in Java

yes

user defined functions

Triggers

yes (cache interceptors and events)

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

SAP/Sybase Replication Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

yes

using Azure authentication

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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