DBMS > Databricks vs. etcd vs. Microsoft Azure Cosmos DB vs. OpenEdge vs. Oracle Rdb

System Properties Comparison Databricks vs. etcd vs. Microsoft Azure Cosmos DB vs. OpenEdge vs. Oracle Rdb

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 distributed reliable key-value store

Globally distributed, horizontally scalable, multi-model database service

Application development environment with integrated database management system

Primary database model

Document store
Relational DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

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	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Website

www.databricks.com

etcd.io
github.com/etcd-io/etcd

azure.microsoft.com/services/cosmos-db

www.progress.com/openedge

www.oracle.com/database/technologies/related/rdb.html

Technical documentation

docs.databricks.com

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

learn.microsoft.com/azure/cosmos-db

documentation.progress.com/output/ua/OpenEdge_latest

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Databricks

Microsoft

Progress Software Corporation

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2013

2014

1984

Current release

3.4, August 2019

OpenEdge 12.2, March 2020

7.4.1.1, 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

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

Server operating systems

hosted

FreeBSD
Linux
Windows

experimental

hosted

AIX
HP-UX
Linux
Solaris
Windows

HP Open VMS

Data scheme

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

schema-free

yes

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

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

with Databricks SQL

SQL-like query language

yes

close to SQL 92

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

gRPC
JSON over HTTP

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC

Supported programming languages

Python
R
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Progress proprietary ABL (Advanced Business Language)

Server-side scripts

Stored procedures

user defined functions and aggregates

JavaScript

yes

Triggers

yes, watching key changes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

since Version 11.4

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

yes

Implicit feature of the cloud service

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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.

User concepts

Access control

Access rights can be defined down to the item level

Users and groups

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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