DBMS > Google BigQuery vs. Hive vs. Teradata vs. Warp 10

System Properties Comparison Google BigQuery vs. Hive vs. Teradata vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

A hybrid cloud data analytics software platform (Teradata Vantage)

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

cloud.google.com/bigquery

hive.apache.org

www.teradata.com

www.warp10.io

Technical documentation

cloud.google.com/bigquery/docs

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

docs.teradata.com

www.warp10.io/content/02_Getting_started

Developer

Google

Apache Software Foundation

initially developed by Facebook

Teradata

SenX

Initial release

2010

2012

1984

2015

Current release

3.1.3, April 2022

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

All OS with a Java VM

hosted
Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

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

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

SQL 2016 + extensions

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
PHP
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

yes

UDFs, stored procedures, table functions in parallel

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Hashing

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

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

Eventual Consistency

Immediate Consistency

based on HBase

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.

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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