DBMS > Geode vs. Google BigQuery vs. HEAVY.AI vs. Kinetica vs. SAP SQL Anywhere

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

Editorial information provided by DB-Engines

Name

Description

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

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

Fully vectorized database across both GPUs and CPUs

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

Primary database model

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

geode.apache.org

cloud.google.com/bigquery

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

www.kinetica.com

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

Technical documentation

geode.apache.org/docs

cloud.google.com/bigquery/docs

docs.heavy.ai

docs.kinetica.com

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Google

HEAVY.AI, Inc.

Kinetica

SAP

formerly Sybase

Initial release

2002

2010

2016

2012

1992

Current release

1.1, February 2017

5.10, January 2022

7.1, August 2021

17, July 2015

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

Open Source

Apache Version 2; enterprise edition 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

Java

C++ and CUDA

C, C++

Server operating systems

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

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

SQL

Support of SQL

SQL-like query language (OQL)

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

Java Client API
Memcached protocol
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

.Net
All JVM based languages
C++
Groovy
Java
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

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

Triggers

yes

Cache Event Listeners

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Round robin

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes, on a single node

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

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights per client and object definable

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 for users and roles on table level

fine grained access rights according to SQL-standard

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