DBMS > Databricks vs. Kdb vs. TypeDB

System Properties Comparison Databricks vs. Kdb vs. TypeDB

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Editorial information provided by DB-Engines

Name

Description

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.

High performance Time Series DBMS

TypeDB provides developers with an expressive, customizable type system to manage their data using an award-winning query language, TypeQL, while building on a high-performance, distributed architecture.

Primary database model

Document store
Relational DBMS

Time Series DBMS
Vector DBMS

Graph DBMS

The type-theoretic data model of TypeDB subsumes the graph database model.
Object oriented DBMS

The data model of TypeDB comprises object-oriented features such as class inheritance and interfaces.
Relational DBMS

The type-theoretic data model of TypeDB subsumes the relational database model.

Secondary database models

Relational DBMS

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

Score	99.29
Rank	#12	Overall
	#2	Document stores
	#8	Relational DBMS

Score	6.49
Rank	#53	Overall
	#2	Time Series DBMS
	#4	Vector DBMS

Score	0.58
Rank	#234	Overall
	#20	Graph DBMS
	#10	Object oriented DBMS
	#109	Relational DBMS

Website

www.databricks.com

kx.com

typedb.com

Technical documentation

docs.databricks.com

code.kx.com

typedb.com/docs

Developer

Databricks

Kx Systems, a division of First Derivatives plc

Vaticle

Initial release

2013

2000

kdb was released 2000, kdb+ in 2003

2016

Current release

3.6, May 2018

2.28.3, June 2024

License

Commercial or Open Source

commercial

free 32-bit version

Open Source

GPL Version 3, commercial licenses 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

Java

Server operating systems

hosted

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

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

yes

table attribute 'grouped'

yes

SQL

Support of SQL

with Databricks SQL

SQL-like query language (q)

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

gRPC protocol
TypeDB Console (shell)
TypeDB Studio (IDE)

Supported programming languages

Python
R
Scala

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

All JVM based languages
C
C++
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

Triggers

yes

with views

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Synchronous replication via raft

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

substituted by the relationship feature

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

User concepts

Access control

rights management via user accounts

yes

at REST API level; other APIs in progress

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More resources

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Recent citations in the news

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