DBMS > Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. Spark SQL vs. SQLite vs. Warp 10

System Properties Comparison Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. Spark SQL vs. SQLite vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

Spark SQL is a component on top of 'Spark Core' for structured data processing

Widely used embeddable, in-process RDBMS

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.92
Rank	#73	Overall
	#6	Wide column stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	116.01
Rank	#10	Overall
	#7	Relational DBMS

Score	0.08
Rank	#358	Overall
	#35	Time Series DBMS

Website

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

spark.apache.org/sql

www.sqlite.org

www.warp10.io

Technical documentation

learn.microsoft.com/azure/cosmos-db

spark.apache.org/docs/latest/sql-programming-guide.html

www.sqlite.org/docs.html

www.warp10.io/content/02_Getting_started

Developer

Microsoft

Apache Software Foundation

Dwayne Richard Hipp

SenX

Initial release

2014

2012

2014

2000

2015

Current release

3.5.0 ( 2.13), September 2023

3.45.2 (12 March 2024), March 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

Java

Server operating systems

hosted

Linux
OS X
Windows

server-less

Linux
OS X
Windows

Data scheme

schema-free

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

not rigid because of 'dynamic typing' concept.

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.

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

RESTful HTTP API

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
Python
R
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

JavaScript

yes

WarpScript

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

yes, utilizing Spark Core

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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