DBMS > Apache Doris vs. Databricks vs. Immudb vs. SAP HANA

System Properties Comparison Apache Doris vs. Databricks vs. Immudb vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

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.

An open source immutable (append-only) database with cryptographic verification which makes it tamper-resistant and fully auditable.

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Relational DBMS

Document store
Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Relational DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.60
Rank	#247	Overall
	#113	Relational DBMS

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	0.31
Rank	#295	Overall
	#42	Key-value stores

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

www.databricks.com

github.com/codenotary/immudb
immudb.io

www.sap.com/products/hana.html

Technical documentation

github.com/apache/doris/wiki

docs.databricks.com

docs.immudb.io

help.sap.com/hana

Developer

Apache Software Foundation, originally contributed from Baidu

Databricks

Codenotary

SAP

Initial release

2017

2013

2020

2010

Current release

1.2.2, February 2023

1.2.3, April 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux

hosted

BSD
Linux
macOS
Solaris
Windows
z/OS

Appliance or cloud-service

Data scheme

yes

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

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

SQL

Support of SQL

yes

with Databricks SQL

SQL-like syntax

yes

APIs and other access methods

JDBC
MySQL client

JDBC
ODBC
RESTful HTTP API

gRPC protocol
PostgreSQL wire protocol

JDBC
ODBC

Supported programming languages

Java

Python
R
Scala

.Net
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

user defined functions and aggregates

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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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