DBMS > Databricks vs. Ehcache vs. SAP HANA

System Properties Comparison Databricks vs. Ehcache vs. SAP HANA

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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.

A widely adopted Java cache with tiered storage options

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

Primary database model

Document store
Relational DBMS

Key-value 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	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

www.databricks.com

www.ehcache.org

www.sap.com/products/hana.html

Technical documentation

docs.databricks.com

www.ehcache.org/documentation

help.sap.com/hana

Developer

Databricks

Terracotta Inc, owned by Software AG

SAP

Initial release

2013

2009

2010

Current release

3.10.0, March 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

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

Java

Server operating systems

hosted

All OS with a Java VM

Appliance or cloud-service

Data scheme

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

with Databricks SQL

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JCache

JDBC
ODBC

Supported programming languages

Python
R
Scala

Java

Server-side scripts

Stored procedures

user defined functions and aggregates

SQLScript, R

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

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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