DBMS > Apache Phoenix vs. BigObject vs. Databricks vs. Machbase Neo vs. Postgres-XL

System Properties Comparison Apache Phoenix vs. BigObject vs. Databricks vs. Machbase Neo vs. Postgres-XL

Editorial information provided by DB-Engines

Name

Description

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

Analytic DBMS for real-time computations and queries

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.

TimeSeries DBMS for AIoT and BigData

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store
Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

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

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Website

Technical documentation

www.postgres-xl.org/documentation

Developer

Apache Software Foundation

BigObject, Inc.

Databricks

Machbase

Initial release

2014

2015

2013

2014

since 2012, originally named StormDB

Current release

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

V8.0, August 2023

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

commercial

free test version available

Open Source

Mozilla 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

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

Linux
macOS
Windows

Linux
macOS

Data scheme

yes

late-bound, schema-on-read capabilities

yes

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

with Databricks SQL

SQL-like query language

yes

distributed, parallel query execution

APIs and other access methods

JDBC

fluentd
ODBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

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

Python
R
Scala

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

Lua

user defined functions and aggregates

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

yes

selectable replication factor

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

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

volatile and lookup table

User concepts

Access control

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

simple password-based access control

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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More resources

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Recent citations in the news

Supercharge SQL on Your Data in Apache HBase with Apache Phoenix | Amazon Web Services
2 June 2016, AWS Blog

Bridge the SQL-NoSQL gap with Apache Phoenix
4 February 2016, InfoWorld

Apache Calcite, FreeMarker, Gora, Phoenix, and Solr updated
27 March 2017, SDTimes.com

Azure HDInsight Analytics Platform Now Supports Apache Hadoop 3.0
18 April 2019, eWeek

Deep dive into Azure HDInsight 4.0
25 September 2018, azure.microsoft.com

provided by Google News

Why Databricks' Tabular Play Has Put Snowflake On The Defensive
10 June 2024, Forbes

Snowflake, DataBricks and the Fight for Apache Iceberg Tables
10 June 2024, The New Stack

John Snow Labs Named 2024 Databricks Growth Data Partner of the Year
11 June 2024, Yahoo Finance

Apple Eyes Deals with Google and Anthropic After OpenAI; Apple Grades Its LLMs While Databricks Grades AI Usage
11 June 2024, The Information

Informatica rolls out new integrations for Databricks’ cloud data platform
10 June 2024, SiliconANGLE News

provided by Google News

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