DBMS > Google BigQuery vs. Heroic vs. SpatiaLite vs. Tibero

System Properties Comparison Google BigQuery vs. Heroic vs. SpatiaLite vs. Tibero

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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

Spatial extension of SQLite

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Secondary database models

Relational DBMS

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	1.72
Rank	#149	Overall
	#3	Spatial DBMS

Score	1.81
Rank	#143	Overall
	#65	Relational DBMS

Website

cloud.google.com/bigquery

github.com/spotify/heroic

www.gaia-gis.it/fossil/libspatialite/index

us.tmaxsoft.com/products/tibero

Technical documentation

cloud.google.com/bigquery/docs

spotify.github.io/heroic

www.gaia-gis.it/gaia-sins/spatialite_topics.html

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

Developer

Google

Spotify

Alessandro Furieri

TmaxSoft

Initial release

2010

2014

2008

2003

Current release

5.0.0, August 2020

6, April 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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++

C and Assembler

Server operating systems

hosted

server-less

AIX
HP-UX
Linux
Solaris
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

APIs and other access methods

RESTful HTTP/JSON API

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

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

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Persistent Stored Procedure (PSM)

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

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.

yes

planned for next version

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

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

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