DBMS > Databricks vs. FeatureBase vs. Hawkular Metrics vs. Postgres-XL

System Properties Comparison Databricks vs. FeatureBase vs. Hawkular Metrics vs. Postgres-XL

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Editorial information provided by DB-Engines

Name

Description

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.

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

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

Primary database model

Document store
Relational DBMS

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	84.24
Rank	#14	Overall
	#2	Document stores
	#9	Relational DBMS

Score	0.23
Rank	#296	Overall
	#134	Relational DBMS

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	0.43
Rank	#260	Overall
	#119	Relational DBMS

Website

Technical documentation

docs.databricks.com

docs.featurebase.com

www.hawkular.org/hawkular-metrics/docs/user-guide

www.postgres-xl.org/documentation

Developer

Databricks

Molecula and Pilosa Open Source Contributors

Community supported by Red Hat

Initial release

2013

2017

2014

since 2012, originally named StormDB

Current release

2022, May 2022

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

hosted

Linux
macOS

Linux
OS X
Windows

Linux
macOS

Data scheme

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

yes

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

with Databricks SQL

SQL queries

yes

distributed, parallel query execution

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

gRPC
JDBC
Kafka Connector
ODBC

HTTP REST

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

Supported programming languages

Python
R
Scala

Java
Python

Go
Java
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using Linux fsync

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

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

DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2023
2 January 2024, Matthias Gelbmann, Paul Andlinger

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Recent citations in the news

Saudi Arabia’s Sovereign Wealth Fund’s Big AI Bets Include Mistral And Databricks
24 September 2024, Forbes

Databricks could launch IPO in two months but biding time despite investor pressure, CEO says
13 September 2024, ION Analytics

Databricks reportedly paid $2 billion in Tabular acquisition
14 August 2024, TechCrunch

Inside the Snowflake — Databricks Rivalry, and Why Both Fear Microsoft
14 August 2024, Bloomberg

The People in Charge at Databricks as It Moves Toward a Potential IPO
24 July 2024, The Information

provided by Google News

Former Silicon Labs CEO Tyson Tuttle launches AI startup Circuit, acquires Austin startup Molecula
23 July 2024, The Business Journals

Get Your Infrastructure Ready for Real-Time Analytics
9 March 2022, Built In

Pilosa: A Scalable High Performance Bitmap Database Index
17 June 2019, hackernoon.com

provided by Google News

Waiting for Red Hat OpenShift 4.0? Too late, 4.1 has already arrived…
5 June 2019, DevClass

provided by Google News

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