DBMS > Databricks vs. SAP HANA vs. Sequoiadb

System Properties Comparison Databricks vs. SAP HANA vs. Sequoiadb

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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.

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

NewSQL database with distributed OLTP and SQL

Primary database model

Document store
Relational DBMS

Relational DBMS

Document store
Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	85.60
Rank	#14	Overall
	#2	Document stores
	#9	Relational DBMS

Score	41.21
Rank	#22	Overall
	#15	Relational DBMS

Score	0.41
Rank	#265	Overall
	#42	Document stores
	#122	Relational DBMS

Website

www.databricks.com

www.sap.com/products/hana.html

www.sequoiadb.com

Technical documentation

docs.databricks.com

help.sap.com/hana

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Databricks

SAP

Sequoiadb Ltd.

Initial release

2013

2010

2013

Current release

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

Server: AGPL; Client: Apache V2

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

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Database as a Service

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Implementation language

C++

Server operating systems

hosted

Appliance or cloud-service

Linux

Data scheme

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

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

with Databricks SQL

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

proprietary protocol using JSON

Supported programming languages

Python
R
Scala

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

SQLScript, R

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Document is locked during a transaction

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

yes

simple password-based access control

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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