DBMS > Amazon Redshift vs. Databricks vs. Graphite vs. GridDB vs. IBM Db2

System Properties Comparison Amazon Redshift vs. Databricks vs. Graphite vs. GridDB vs. IBM Db2

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

Scalable in-memory time series database optimized for IoT and Big Data

Common in IBM host environments, 2 different versions for host and Windows/Linux

Primary database model

Relational DBMS

Document store
Relational DBMS

Time Series DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Secondary database models

Key-value store
Relational DBMS

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	4.57
Rank	#73	Overall
	#5	Time Series DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Website

aws.amazon.com/redshift

www.databricks.com

github.com/graphite-project/graphite-web

griddb.net

www.ibm.com/products/db2

Technical documentation

docs.aws.amazon.com/redshift

docs.databricks.com

graphite.readthedocs.io

docs.griddb.net

www.ibm.com/docs/en/db2

Developer

Amazon (based on PostgreSQL)

Databricks

Chris Davis

Toshiba Corporation

IBM

Initial release

2012

2013

2006

2013

1983

host version

Current release

5.1, August 2022

12.1, October 2016

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

commercial

free version is available

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

Python

C++

C and C++

Server operating systems

hosted

Linux
Unix

Linux

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

yes

Numeric data only

yes

numerical, string, blob, geometry, boolean, timestamp

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

with Databricks SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

HTTP API
Sockets

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

JavaScript (Node.js)
Python

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

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

only with Windows/Unix/Linux Version

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication

yes

with separate tools (MQ, InfoSphere)

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate consistency within container, eventual consistency across containers

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

ACID at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes

locking

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

fine grained access rights according to SQL-standard

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
» more

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.