DBMS > Google BigQuery vs. Graph Engine vs. Hazelcast vs. InterSystems Caché

System Properties Comparison Google BigQuery vs. Graph Engine vs. Hazelcast vs. InterSystems Caché

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Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

Large scale data warehouse service with append-only tables

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A widely adopted in-memory data grid

A multi-model DBMS and application server

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Key-value store

Key-value store
Object oriented DBMS
Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Website

cloud.google.com/bigquery

www.graphengine.io

hazelcast.com

www.intersystems.com/products/cache

Technical documentation

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

hazelcast.org/imdg/docs

docs.intersystems.com

Developer

Google

Microsoft

Hazelcast

InterSystems

Initial release

2010

2008

1997

Current release

5.3.6, November 2023

2018.1.4, May 2020

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

Apache Version 2; commercial licenses 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

.NET and C

Java

Server operating systems

hosted

.NET

All OS with a Java VM

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

depending on used data model

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

.NET Client API
JDBC
ODBC
RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

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

C#
C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

yes

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Source-replica replication

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

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

Access rights for users, groups and roles

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