DBMS > Google BigQuery vs. HEAVY.AI vs. Hypertable vs. SAP SQL Anywhere

System Properties Comparison Google BigQuery vs. HEAVY.AI vs. Hypertable vs. SAP SQL Anywhere

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

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

An open source BigTable implementation based on distributed file systems such as Hadoop

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

cloud.google.com/bigquery

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

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

cloud.google.com/bigquery/docs

docs.heavy.ai

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Google

HEAVY.AI, Inc.

Hypertable Inc.

SAP

formerly Sybase

Initial release

2010

2016

2009

1992

Current release

5.10, January 2022

0.9.8.11, March 2016

17, July 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

GNU version 3. Commercial license 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

C++ and CUDA

C++

Server operating systems

hosted

Linux

Linux
OS X
Windows

an inofficial Windows port is available

AIX
HP-UX
Linux
OS X
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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

C++ API
Thrift

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor on file system level

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

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

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