DBMS > Apache Impala vs. Cubrid vs. Google BigQuery vs. TDengine

System Properties Comparison Apache Impala vs. Cubrid vs. Google BigQuery vs. TDengine

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Large scale data warehouse service with append-only tables

Time Series DBMS and big data platform

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Document store

Relational DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.68
Rank	#106	Overall
	#9	Time Series DBMS

Website

impala.apache.org

cubrid.com (korean)
cubrid.org (english)

cloud.google.com/bigquery

github.com/taosdata/TDengine
tdengine.com

Technical documentation

impala.apache.org/impala-docs.html

cubrid.org/manuals

cloud.google.com/bigquery/docs

docs.tdengine.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

CUBRID Corporation, CUBRID Foundation

Google

TDEngine, previously Taos Data

Initial release

2013

2008

2010

2019

Current release

4.1.0, June 2022

11.0, January 2021

3.0, August 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

Open Source

AGPL V3, also commercial editions available

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, C++, Java

Server operating systems

Linux

Linux
Windows

hosted

Linux
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

Standard SQL with extensions for time-series applications

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP/JSON API

JDBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures

user defined functions

in JavaScript

Triggers

yes

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

yes

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

Eventual Consistency

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.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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)

yes

More information provided by the system vendor

Specific characteristics

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

High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
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Typical application scenarios

TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
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Market metrics

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