DBMS > AlaSQL vs. Google BigQuery vs. Interbase vs. Kyligence Enterprise vs. Microsoft Azure Cosmos DB

System Properties Comparison AlaSQL vs. Google BigQuery vs. Interbase vs. Kyligence Enterprise vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Large scale data warehouse service with append-only tables

Light-weight proven RDBMS

originally from Borland

A distributed analytics engine for big data, built on top of Apache Kylin

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store
Relational DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

alasql.org

cloud.google.com/bigquery

www.embarcadero.com/products/interbase

kyligence.io/kyligence-enterprise

azure.microsoft.com/services/cosmos-db

Technical documentation

github.com/AlaSQL/alasql

cloud.google.com/bigquery/docs

docs.embarcadero.com/products/interbase

learn.microsoft.com/azure/cosmos-db

Developer

Andrey Gershun & Mathias R. Wulff

Google

Embarcadero

Kyligence, Inc.

Microsoft

Initial release

2014

2010

1984

2016

2014

Current release

InterBase 2020, December 2019

License

Commercial or Open Source

Open Source

MIT-License

commercial

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

JavaScript

Java

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

hosted

Android
iOS
Linux
OS X
Windows

Linux

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON 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.

export as XML data possible

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

APIs and other access methods

JavaScript API

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

JavaScript

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Interbase procedure and trigger language

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

Interbase Change Views

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

only for local storage and DOM-storage

Since BigQuery is designed for querying data

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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