DBMS > Apache Druid vs. GridGain vs. Realm vs. Snowflake

System Properties Comparison Apache Druid vs. GridGain vs. Realm vs. Snowflake

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Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

Primary database model

Relational DBMS
Time Series DBMS

Key-value store
Relational DBMS

Document store

Relational DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

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

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

Technical documentation

druid.apache.org/docs/latest/design

www.gridgain.com/docs/index.html

realm.io/docs

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation and contributors

GridGain Systems, Inc.

Realm, acquired by MongoDB in May 2019

Snowflake Computing Inc.

Initial release

2012

2007

2014

Current release

29.0.1, April 2024

GridGain 8.5.1

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

commercial

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

Java, C++, .Net

Server operating systems

Linux
OS X
Unix

Linux
OS X
Solaris
Windows

Android
Backend: server-less
iOS
Windows

hosted

Data scheme

yes

schema-less columns are supported

yes

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

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 for querying

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

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

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

CLI Client
JDBC
ODBC

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

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

runs within the applications so server-side scripts are unnecessary

user defined functions

Triggers

yes (cache interceptors and events)

yes

Change Listeners

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes (replicated cache)

none

yes

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

Foreign keys

Referential integrity

yes

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

In-Memory realm

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Security Hooks for custom implementations

yes

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

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