DBMS > Amazon DynamoDB vs. etcd vs. Kinetica vs. SAP SQL Anywhere vs. TimescaleDB

System Properties Comparison Amazon DynamoDB vs. etcd vs. Kinetica vs. SAP SQL Anywhere vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

A distributed reliable key-value store

Fully vectorized database across both GPUs and CPUs

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store
Key-value store

Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

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

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

aws.amazon.com/dynamodb

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

www.kinetica.com

www.sap.com/products/technology-platform/sql-anywhere.html

www.timescale.com

Technical documentation

docs.aws.amazon.com/dynamodb

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

docs.kinetica.com

help.sap.com/docs/SAP_SQL_Anywhere

docs.timescale.com

Developer

Amazon

Kinetica

SAP

formerly Sybase

Timescale

Initial release

2012

1992

2017

Current release

3.4, August 2019

7.1, August 2021

17, July 2015

2.15.0, May 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

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++

Server operating systems

hosted

FreeBSD
Linux
Windows

experimental

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

full PostgreSQL SQL syntax

APIs and other access methods

RESTful HTTP API

gRPC
JSON over HTTP

JDBC
ODBC
RESTful HTTP API

ADO.NET
HTTP API
JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

C++
Java
JavaScript (Node.js)
Python

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

yes, in C/C++, Java, .Net or Perl

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

by integration with AWS Lambda

yes, watching key changes

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, across time and space (hash partitioning) attributes

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.

Source-replica replication

Database mirroring

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

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

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.