DBMS > Coveo vs. Hive vs. Microsoft Azure Table Storage vs. Oracle vs. Spark SQL

System Properties Comparison Coveo vs. Hive vs. Microsoft Azure Table Storage vs. Oracle vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A Wide Column Store for rapid development using massive semi-structured datasets

Widely used RDBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	2.11
Rank	#118	Overall
	#11	Search engines

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.coveo.com

hive.apache.org

azure.microsoft.com/en-us/services/storage/tables

www.oracle.com/database

spark.apache.org/sql

Technical documentation

docs.coveo.com

cwiki.apache.org/confluence/display/Hive/Home

docs.oracle.com/en/database

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Coveo

Apache Software Foundation

initially developed by Facebook

Microsoft

Oracle

Apache Software Foundation

Initial release

2012

1980

2014

Current release

3.1.3, April 2022

23c, September 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Scala

Server operating systems

hosted

All OS with a Java VM

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
OS X
Windows

Data scheme

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

schema-free

yes

Schemaless in JSON and XML columns

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 DML and DDL statements

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Thrift

RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JDBC
ODBC

Supported programming languages

C#
Java
JavaScript
Python

C++
Java
PHP
Python

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

optimistic locking

ACID

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

granular access controls, API key management, content filters

Access rights for users, groups and roles

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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