DBMS > etcd vs. Heroic vs. jBASE vs. Microsoft Azure Synapse Analytics vs. Transwarp KunDB

System Properties Comparison etcd vs. Heroic vs. jBASE vs. Microsoft Azure Synapse Analytics vs. Transwarp KunDB

Editorial information provided by DB-Engines

Name

Description

A distributed reliable key-value store

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A robust multi-value DBMS comprising development tools and middleware

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

OLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle

Primary database model

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

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.41
Rank	#159	Overall
	#3	Multivalue DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.10
Rank	#344	Overall
	#151	Relational DBMS

Website

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

github.com/spotify/heroic

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

azure.microsoft.com/services/synapse-analytics

www.transwarp.cn/en/product/kundb

Technical documentation

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

spotify.github.io/heroic

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

docs.microsoft.com/azure/synapse-analytics

Developer

Spotify

Rocket Software (formerly Zumasys)

Microsoft

Transwarp

Initial release

2014

1991

2016

Current release

3.4, August 2019

5.7

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 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

Java

C++

Server operating systems

FreeBSD
Linux
Windows

experimental

AIX
Linux
Windows

hosted

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

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

via Elasticsearch

yes

SQL

Support of SQL

Embedded SQL for jBASE in BASIC

yes

APIs and other access methods

gRPC
JSON over HTTP

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

ADO.NET
JDBC
ODBC

Supported programming languages

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

.Net
Basic
Jabbascript
Java

C#
Java
PHP

Server-side scripts

Stored procedures

yes

Transact SQL

yes

Triggers

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

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.

yes

User concepts

Access control

Access rights can be defined down to the item level

yes

More information provided by the system vendor

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