DBMS > Google BigQuery vs. Hazelcast vs. HEAVY.AI vs. SQLite

System Properties Comparison Google BigQuery vs. Hazelcast vs. HEAVY.AI vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A widely adopted in-memory data grid

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

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Website

cloud.google.com/bigquery

hazelcast.com

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

www.sqlite.org

Technical documentation

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

docs.heavy.ai

www.sqlite.org/docs.html

Developer

Google

Hazelcast

HEAVY.AI, Inc.

Dwayne Richard Hipp

Initial release

2010

2008

2016

2000

Current release

5.3.6, November 2023

5.10, January 2022

3.46.1 (13 August 2024), August 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2; enterprise edition available

Open Source

Public Domain

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

Server operating systems

hosted

All OS with a Java VM

Linux

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

All languages supporting JDBC/ODBC/Thrift
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

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

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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)

Role-based access control

fine grained access rights according to SQL-standard

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