DBMS > Databricks vs. EsgynDB vs. GBase vs. Geode

System Properties Comparison Databricks vs. EsgynDB vs. GBase vs. Geode

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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.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Primary database model

Document store
Relational DBMS

Relational DBMS

Key-value store

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

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	1.86
Rank	#134	Overall
	#24	Key-value stores

Website

Technical documentation

docs.databricks.com

geode.apache.org/docs

Developer

Databricks

Esgyn

General Data Technology Co., Ltd.

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Initial release

2013

2015

2004

2002

Current release

GBase 8a, GBase 8s, GBase 8c

1.1, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available as Gemfire

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

C++, Java

C, Java, Python

Java

Server operating systems

hosted

Linux

All OS with a Java VM

the JDK (8 or later) is also required

Data scheme

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

yes

schema-free

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

Standard with numerous extensions

SQL-like query language (OQL)

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

ADO.NET
C API
JDBC
ODBC

Java Client API
Memcached protocol
RESTful HTTP API

Supported programming languages

Python
R
Scala

All languages supporting JDBC/ODBC/ADO.Net

.Net
All JVM based languages
C++
Groovy
Java
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

Java Stored Procedures

user defined functions

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication between multi datacenters

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

yes, on a single node

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

Access rights per client and object definable

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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