DBMS > Apache Phoenix vs. ArcadeDB vs. Databricks vs. Google BigQuery vs. H2

System Properties Comparison Apache Phoenix vs. ArcadeDB vs. Databricks vs. Google BigQuery vs. H2

Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Large scale data warehouse service with append-only tables

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Document store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.10
Rank	#358	Overall
	#48	Document stores
	#38	Graph DBMS
	#52	Key-value stores
	#35	Time Series DBMS

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Website

phoenix.apache.org

arcadedb.com

www.databricks.com

cloud.google.com/bigquery

www.h2database.com

Technical documentation

phoenix.apache.org

docs.arcadedb.com

docs.databricks.com

cloud.google.com/bigquery/docs

www.h2database.com/html/main.html

Developer

Apache Software Foundation

Arcade Data

Databricks

Google

Thomas Mueller

Initial release

2014

2021

2013

2010

2005

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

September 2021

2.2.220, July 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

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

Server operating systems

Linux
Unix
Windows

All OS with a Java VM

hosted

All OS with a Java VM

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

Flexible Schema (defined schema, partial schema, schema free)

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

yes

SQL-like query language, no joins

with Databricks SQL

yes

APIs and other access methods

JDBC

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

Java

Python
R
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

Server-side scripts

Stored procedures

user defined functions

user defined functions and aggregates

user defined functions

in JavaScript

Java Stored Procedures and User-Defined Functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

yes

With clustering: 2 database servers on different computers operate on identical copies of a database

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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