DBMS > EsgynDB vs. Google Cloud Bigtable vs. Hive vs. SiteWhere

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. Hive vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Key-value store
Wide column store

Relational DBMS

Time Series DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

www.esgyn.cn

cloud.google.com/bigtable

hive.apache.org

github.com/sitewhere/sitewhere

Technical documentation

cloud.google.com/bigtable/docs

cwiki.apache.org/confluence/display/Hive/Home

sitewhere1.sitewhere.io/index.html

Developer

Esgyn

Google

Apache Software Foundation

initially developed by Facebook

SiteWhere

Initial release

2015

2012

2010

Current release

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

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++, Java

Java

Server operating systems

Linux

hosted

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

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

JDBC
ODBC
Thrift

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

C++
Java
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

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

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

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

fine grained access rights according to SQL-standard

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

Access rights for users, groups and roles

Users with fine-grained authorization concept

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More resources

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