DBMS > BigObject vs. etcd vs. Microsoft Azure Synapse Analytics vs. Trafodion

System Properties Comparison BigObject vs. etcd vs. Microsoft Azure Synapse Analytics vs. Trafodion

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Analytic DBMS for real-time computations and queries

A distributed reliable key-value store

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Relational DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Website

bigobject.io

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

azure.microsoft.com/services/synapse-analytics

trafodion.apache.org

Technical documentation

docs.bigobject.io

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

docs.microsoft.com/azure/synapse-analytics

trafodion.apache.org/documentation.html

Developer

BigObject, Inc.

Microsoft

Apache Software Foundation, originally developed by HP

Initial release

2015

2016

2014

Current release

3.4, August 2019

2.3.0, February 2019

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 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

C++

C++, Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
Windows

experimental

hosted

Linux

Data scheme

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

gRPC
JSON over HTTP

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

C#
Java
PHP

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

Lua

Transact SQL

Java Stored Procedures

Triggers

yes, watching key changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

yes

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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

Read/write lock on objects (tables, trees)

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

yes

fine grained access rights according to SQL-standard

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