DBMS > Google BigQuery vs. Oracle Coherence vs. Riak KV vs. Vertica

System Properties Comparison Google BigQuery vs. Oracle Coherence vs. Riak KV vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Oracles in-memory data grid solution

Distributed, fault tolerant key-value store

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

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	1.90
Rank	#127	Overall
	#21	Key-value stores

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

cloud.google.com/bigquery

www.oracle.com/java/coherence

www.vertica.com

Technical documentation

cloud.google.com/bigquery/docs

docs.oracle.com/en/middleware/standalone/coherence

www.tiot.jp/riak-docs/riak/kv/latest

vertica.com/documentation

Developer

Google

Oracle

OpenSource, formerly Basho Technologies

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2010

2007

2009

2005

Current release

14.1, August 2023

3.2.0, December 2022

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

C++

Server operating systems

hosted

All OS with a Java VM

Linux
OS X

Linux

Data scheme

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

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.

Secondary indexes

restricted

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

SQL

Support of SQL

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
RESTful HTTP API

HTTP API
Native Erlang Interface

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++
Java

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Erlang

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

Live Events

yes

pre-commit hooks and post-commit hooks

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

no "single point of failure"

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with selectable consistency level

selectable replication factor

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

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

yes

Transaction concepts

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

Since BigQuery is designed for querying data

configurable

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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)

authentification to access the cache via certificates or http basic authentication

yes, using Riak Security

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

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