DBMS > Apache IoTDB vs. GBase vs. Hazelcast vs. Teradata Aster

System Properties Comparison Apache IoTDB vs. GBase vs. Hazelcast vs. Teradata Aster

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Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

A widely adopted in-memory data grid

Platform for big data analytics on multistructured data sources and types

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Website

iotdb.apache.org

www.gbase.cn

hazelcast.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

hazelcast.org/imdg/docs

Developer

Apache Software Foundation

General Data Technology Co., Ltd.

Hazelcast

Teradata

Initial release

2018

2004

2008

2005

Current release

1.1.0, April 2023

GBase 8a, GBase 8s, GBase 8c

5.3.6, November 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C, Java, Python

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

All OS with a Java VM

Linux

Data scheme

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

the object must implement a serialization strategy

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

Standard with numerous extensions

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

ADO.NET
C API
JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

user defined functions

yes

Event Listeners, Executor Services

R packages

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

Replicated Map

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

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

yes

Role-based access control

fine grained access rights according to SQL-standard

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