DBMS > Brytlyt vs. Citus vs. Google BigQuery vs. HEAVY.AI vs. SiteWhere

System Properties Comparison Brytlyt vs. Citus vs. Google BigQuery vs. HEAVY.AI vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Large scale data warehouse service with append-only tables

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Document store

Spatial DBMS

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

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

brytlyt.io

www.citusdata.com

cloud.google.com/bigquery

github.com/heavyai/heavydb
www.heavy.ai

github.com/sitewhere/sitewhere

Technical documentation

docs.brytlyt.io

docs.citusdata.com

cloud.google.com/bigquery/docs

docs.heavy.ai

sitewhere1.sitewhere.io/index.html

Developer

Brytlyt

Google

HEAVY.AI, Inc.

SiteWhere

Initial release

2016

2010

2016

2010

Current release

5.0, August 2023

8.1, December 2018

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

AGPL, commercial license also available

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Common Public Attribution License Version 1.0

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++ and CUDA

C++ and CUDA

Java

Server operating systems

Linux
OS X
Windows

Linux

hosted

Linux

Linux
OS X
Windows

Data scheme

yes

predefined scheme

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

specific XML-type available, but no XML query functionality.

yes

specific XML type available, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

yes

APIs and other access methods

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

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

HTTP REST

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Round robin

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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