DBMS > Databricks vs. Google BigQuery vs. Percona Server for MongoDB vs. Prometheus

System Properties Comparison Databricks vs. Google BigQuery vs. Percona Server for MongoDB vs. Prometheus

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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.

Large scale data warehouse service with append-only tables

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Open-source Time Series DBMS and monitoring system

Primary database model

Document store
Relational DBMS

Relational DBMS

Document store

Time Series DBMS

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

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Website

www.databricks.com

cloud.google.com/bigquery

www.percona.com/mongodb/software/percona-server-for-mongodb

prometheus.io

Technical documentation

docs.databricks.com

cloud.google.com/bigquery/docs

docs.percona.com/percona-distribution-for-mongodb

prometheus.io/docs

Developer

Databricks

Google

Percona

Initial release

2013

2010

2015

Current release

3.4.10-2.10, November 2017

License

Commercial or Open Source

commercial

Open Source

GPL Version 2

Open Source

Apache 2.0

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

C++

Server operating systems

hosted

Linux

Linux
Windows

Data scheme

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

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

proprietary protocol using JSON

RESTful HTTP/JSON API

Supported programming languages

Python
R
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

none

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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

via In-Memory Engine

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

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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