DBMS > Google BigQuery vs. Hazelcast vs. Microsoft Azure SQL Database vs. UniData,UniVerse vs. Vertica

System Properties Comparison Google BigQuery vs. Hazelcast vs. Microsoft Azure SQL Database vs. UniData,UniVerse vs. Vertica

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A widely adopted in-memory data grid

Database as a Service offering with high compatibility to Microsoft SQL Server

MultiValue database and application server with SQL mapping layer and meta database capabilities

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

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

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	2.85
Rank	#98	Overall
	#1	Multivalue DBMS

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Website

cloud.google.com/bigquery

hazelcast.com

azure.microsoft.com/en-us/products/azure-sql/database

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

www.vertica.com

Technical documentation

cloud.google.com/bigquery/docs

hazelcast.org/imdg/docs

docs.microsoft.com/en-us/azure/azure-sql

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

vertica.com/documentation

Developer

Google

Hazelcast

Microsoft

Rocket Software

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2010

2008

2010

1985

2005

Current release

5.3.6, November 2023

V12

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

hosted

All OS with a Java VM

hosted

AIX
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

optional

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

SQL-like query language

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

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

.Net
Basic

U2 Basic
C
Java

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Event Listeners, Executor Services

Transact SQL

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

Events

yes

U2 Basic

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes, with always 3 replicas available

Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

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

configurable

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)

Role-based access control

fine grained access rights according to SQL-standard

Access rights according to SQL-standard and operating system based

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

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