DBMS > etcd vs. FileMaker vs. Google BigQuery vs. Riak TS vs. Yanza

System Properties Comparison etcd vs. FileMaker vs. Google BigQuery vs. Riak TS vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A distributed reliable key-value store

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

Large scale data warehouse service with append-only tables

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Time Series DBMS for IoT Applications

Primary database model

Key-value store

Relational DBMS

Time Series DBMS

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

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

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

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

www.claris.com/filemaker

cloud.google.com/bigquery

yanza.com

Technical documentation

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

www.claris.com/resources/documentation

cloud.google.com/bigquery/docs

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Claris

a subsidiary of Apple

Google

Open Source, formerly Basho Technologies

Yanza

Initial release

1983

2010

2015

Current release

3.4, August 2019

19.4.1, November 2021

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Erlang

Server operating systems

FreeBSD
Linux
Windows

experimental

iOS

client part only
Linux
OS X
Windows

hosted

Linux
OS X

Windows

Data scheme

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

A http query request can return the data in XML format

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

via plugins

yes

yes, limited

APIs and other access methods

gRPC
JSON over HTTP

Filemaker WebDirect
JDBC
ODBC

RESTful HTTP/JSON API

HTTP API
Native Erlang Interface

HTTP API

Supported programming languages

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

PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Erlang

Triggers

yes, watching key changes

yes

pre-commit hooks and post-commit hooks

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication, warm standby

since Version 14

selectable replication factor

none

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

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

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

Since BigQuery is designed for querying data

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

simple rights management via user accounts and connection to external directory services

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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