DBMS > Google BigQuery vs. HEAVY.AI vs. HyperSQL vs. Microsoft Azure Table Storage

System Properties Comparison Google BigQuery vs. HEAVY.AI vs. HyperSQL vs. Microsoft Azure Table Storage

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

A Wide Column Store for rapid development using massive semi-structured datasets

Primary database model

Relational DBMS

Wide column store

Secondary database models

Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Website

cloud.google.com/bigquery

github.com/heavyai/heavydb
www.heavy.ai

hsqldb.org

azure.microsoft.com/en-us/services/storage/tables

Technical documentation

cloud.google.com/bigquery/docs

docs.heavy.ai

hsqldb.org/web/hsqlDocsFrame.html

Developer

Google

HEAVY.AI, Inc.

Microsoft

Initial release

2010

2016

2001

2012

Current release

5.10, January 2022

2.7.2, June 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

based on BSD license

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

C++ and CUDA

Java

Server operating systems

hosted

Linux

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

hosted

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

HTTP API

JDBC via HTTP
JDBC
ODBC

RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

All languages supporting JDBC/ODBC
Java

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

optimistic locking

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)

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.