DBMS > Amazon Redshift vs. Databricks vs. H2GIS vs. Kinetica

System Properties Comparison Amazon Redshift vs. Databricks vs. H2GIS vs. Kinetica

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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.

Spatial extension of H2

Fully vectorized database across both GPUs and CPUs

Primary database model

Relational DBMS

Document store
Relational DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Website

aws.amazon.com/redshift

www.databricks.com

www.h2gis.org

www.kinetica.com

Technical documentation

docs.aws.amazon.com/redshift

docs.databricks.com

www.h2gis.org/docs/home

docs.kinetica.com

Developer

Amazon (based on PostgreSQL)

Databricks

CNRS

Kinetica

Initial release

2012

2013

2012

Current release

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

LGPL 3.0

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

C, C++

Server operating systems

hosted

Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, 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

with Databricks SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

Java

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

yes

based on H2

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

GPU vRAM or System RAM

User concepts

Access control

fine grained access rights according to SQL-standard

yes

based on H2

Access rights for users and roles on table level

More information provided by the system vendor

Specific characteristics

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