DBMS > OpenEdge vs. Prometheus vs. Snowflake vs. Spark SQL

System Properties Comparison OpenEdge vs. Prometheus vs. Snowflake vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Open-source Time Series DBMS and monitoring system

Cloud-based data warehousing service for structured and semi-structured data

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	3.45
Rank	#85	Overall
	#46	Relational DBMS

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.progress.com/openedge

prometheus.io

www.snowflake.com

spark.apache.org/sql

Technical documentation

documentation.progress.com/output/ua/OpenEdge_latest

prometheus.io/docs

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Progress Software Corporation

Snowflake Computing Inc.

Apache Software Foundation

Initial release

1984

2015

2014

Current release

OpenEdge 12.2, March 2020

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

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

Scala

Server operating systems

AIX
HP-UX
Linux
Solaris
Windows

Linux
Windows

hosted

Linux
OS X
Windows

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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

Import of XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Progress proprietary ABL (Advanced Business Language)

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

since Version 11.4

Sharding

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

by Federation

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

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.

User concepts

Access control

Users and groups

Users with fine-grained authorization concept, user roles and pluggable authentication

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