DBMS > Amazon Redshift vs. Apache Druid vs. BigchainDB vs. GridGain vs. IBM Db2 Event Store

System Properties Comparison Amazon Redshift vs. Apache Druid vs. BigchainDB vs. GridGain vs. IBM Db2 Event Store

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

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

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Document store

Key-value store
Relational DBMS

Event Store
Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

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

Score	0.79
Rank	#212	Overall
	#36	Document stores

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Website

aws.amazon.com/redshift

druid.apache.org

www.bigchaindb.com

www.gridgain.com

www.ibm.com/products/db2-event-store

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

bigchaindb.readthedocs.io/en/latest

www.gridgain.com/docs/index.html

www.ibm.com/docs/en/db2-event-store

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

GridGain Systems, Inc.

IBM

Initial release

2012

2016

2007

2017

Current release

29.0.1, April 2024

GridGain 8.5.1

2.0

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

AGPL v3

commercial

free developer edition available

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

Python

Java, C++, .Net

C and C++

Server operating systems

hosted

Linux
OS X
Unix

Linux

Linux
OS X
Solaris
Windows

Linux

Linux, macOS, Windows for the developer addition

Data scheme

yes

schema-less columns are supported

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

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

yes

through the embedded Spark runtime

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

CLI Client
RESTful HTTP API

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

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Go
Haskell
Java
JavaScript
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

user defined functions

in Python

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

yes

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

selectable replication factor

yes (replicated cache)

Active-active shard replication

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

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

yes

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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