DBMS > Amazon DynamoDB vs. Google Cloud Bigtable vs. SiriDB vs. SiteWhere vs. TimescaleDB

System Properties Comparison Amazon DynamoDB vs. Google Cloud Bigtable vs. SiriDB vs. SiteWhere vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Open Source Time Series DBMS

M2M integration platform for persisting/querying time series data

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
Wide column store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	77.57
Rank	#16	Overall
	#2	Document stores
	#2	Key-value stores

Score	3.58
Rank	#92	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	0.11
Rank	#346	Overall
	#32	Time Series DBMS

Score	0.06
Rank	#367	Overall
	#36	Time Series DBMS

Score	4.87
Rank	#74	Overall
	#4	Time Series DBMS

Website

aws.amazon.com/dynamodb

cloud.google.com/bigtable

siridb.com

github.com/sitewhere/sitewhere

www.timescale.com

Technical documentation

docs.aws.amazon.com/dynamodb

cloud.google.com/bigtable/docs

docs.siridb.com

sitewhere1.sitewhere.io/index.html

docs.timescale.com

Developer

Amazon

Google

Cesbit

SiteWhere

Timescale

Initial release

2012

2015

2017

2010

2017

Current release

2.13.0, November 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

MIT License

Open Source

Common Public Attribution License Version 1.0

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

Java

Server operating systems

hosted

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

predefined scheme

yes

Typing

predefined data types such as float or date

yes

Numeric data

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

yes

full PostgreSQL SQL syntax

APIs and other access methods

RESTful HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

HTTP API

HTTP REST

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

C#
C++
Go
Java
JavaScript (Node.js)
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

selectable replication factor

based on HBase

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)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

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

Atomic single-row operations

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple rights management via user accounts

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

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