DBMS > Microsoft Azure Table Storage vs. Speedb vs. SQream DB

System Properties Comparison Microsoft Azure Table Storage vs. Speedb vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

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

An embeddable, high performance key-value store optimized for write-intensive workloads, which can be used as a drop-in replacement for RocksDB

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Wide column store

Key-value store

Relational DBMS

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

Score	2.66
Rank	#97	Overall
	#7	Wide column stores

Score	0.15
Rank	#328	Overall
	#48	Key-value stores

Score	0.64
Rank	#228	Overall
	#105	Relational DBMS

Website

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

www.speedb.io

sqream.com

Technical documentation

docs.sqream.com

Developer

Microsoft

Speedb

SQream Technologies

Initial release

2012

2020

2017

Current release

2022.1.6, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0; commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

hosted

Linux
Windows

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL 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.

Secondary indexes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

.Net
JDBC
ODBC

Supported programming languages

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

C
C++
Go
Java
Perl
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

optimistic locking

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

Access rights based on private key authentication or shared access signatures

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