DBMS > AntDB vs. Databricks vs. Firebase Realtime Database vs. Microsoft SQL Server vs. TimescaleDB

System Properties Comparison AntDB vs. Databricks vs. Firebase Realtime Database vs. Microsoft SQL Server vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

A scalable, multi-tenant, MPP-architectured RDBMS for OLTP and OLAP operations, highly compatible with Oracle

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.

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Microsofts flagship relational DBMS

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Relational DBMS

Document store
Relational DBMS

Document store

Relational DBMS

Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	0.25
Rank	#303	Overall
	#137	Relational DBMS

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

www.asiainfo.com/en_us/product_aisware_antdb_detail.html

www.databricks.com

firebase.google.com/products/realtime-database

www.microsoft.com/en-us/sql-server

www.timescale.com

Technical documentation

docs.databricks.com

firebase.google.com/docs/database

learn.microsoft.com/en-US/sql/sql-server

docs.timescale.com

Developer

AsiaInfo Technologies Limited

Databricks

Google

acquired by Google 2014

Microsoft

Timescale

Initial release

2013

2012

1989

2017

Current release

SQL Server 2022, November 2022

2.15.0, May 2024

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

C++

Server operating systems

hosted

Linux
Windows

Linux
OS X
Windows

Data scheme

yes

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

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

SQL2016 compliant

with Databricks SQL

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
MySQL protocol compliant
ODBC

JDBC
ODBC
RESTful HTTP API

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

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

Supported programming languages

Python
R
Scala

Java
JavaScript
Objective-C

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

limited functionality with using 'rules'

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

tables can be distributed across several files (horizontal partitioning); sharding through federation

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, but depending on the SQL-Server Edition

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

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

User concepts

Access control

yes, based on authentication and database rules

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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