DBMS > Google BigQuery vs. Heroic vs. TDengine vs. Tibero vs. Valentina Server

System Properties Comparison Google BigQuery vs. Heroic vs. TDengine vs. Tibero vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Time Series DBMS and big data platform

A secure RDBMS, designed for easy portability from Oracle

Object-relational database and reports server

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	2.68
Rank	#106	Overall
	#9	Time Series DBMS

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

cloud.google.com/bigquery

github.com/spotify/heroic

github.com/taosdata/TDengine
tdengine.com

us.tmaxsoft.com/products/tibero

www.valentina-db.net

Technical documentation

cloud.google.com/bigquery/docs

spotify.github.io/heroic

docs.tdengine.com

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Google

Spotify

TDEngine, previously Taos Data

TmaxSoft

Paradigma Software

Initial release

2010

2014

2019

2003

1999

Current release

3.0, August 2022

6, April 2015

5.7.5

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

AGPL V3, also commercial editions available

commercial

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

C and Assembler

Server operating systems

hosted

Linux
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

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

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

Standard SQL with extensions for time-series applications

yes

APIs and other access methods

RESTful HTTP/JSON API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Persistent Stored Procedure (PSM)

yes

Triggers

yes, via alarm monitoring

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

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.

planned for next version

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

yes

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more

Competitive advantages

High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more

Typical application scenarios

TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more

Market metrics

TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more

Licensing and pricing models

TDengine OSS is an open source, cloud native time series database. It includes built-in...
» more

News

Mastering Memory Leak Detection in TDengine
31 May 2024

Seamless Data Integration from MQTT and InfluxDB to TDengine
22 May 2024

Solving Long Query Performance Bottlenecks
22 May 2024

What Is Predictive Maintenance?
17 May 2024

Can Typical Time-Series Databases Replace Data Historians?
8 May 2024

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