DBMS > Apache Kylin vs. EXASOL vs. Faircom DB vs. Google Cloud Datastore vs. Machbase Neo

System Properties Comparison Apache Kylin vs. EXASOL vs. Faircom DB vs. Google Cloud Datastore vs. Machbase Neo

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

TimeSeries DBMS for AIoT and BigData

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Document store

Time Series DBMS

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

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Website

kylin.apache.org

www.exasol.com

www.faircom.com/products/faircom-db

cloud.google.com/datastore

machbase.com

Technical documentation

kylin.apache.org/docs

www.exasol.com/resources

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

cloud.google.com/datastore/docs

machbase.com/dbms

Developer

Apache Software Foundation, originally contributed from eBay Inc

Exasol

FairCom Corporation

Google

Machbase

Initial release

2015

2000

1979

2008

2013

Current release

3.1.0, July 2020

V12, November 2020

V8.0, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Restricted, free version available

commercial

free test version available

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

Java

ANSI C, C++

Server operating systems

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

Linux
macOS
Windows

Data scheme

yes

schema free, schema optional, schema required, partial schema,

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

yes, details here

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

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

yes

yes, ANSI SQL with proprietary extensions

SQL-like query language (GQL)

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

Supported programming languages

Java
Lua
Python
R

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Server-side scripts

Stored procedures

user defined functions

yes

.Net, JavaScript, C/C++

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Multi-source replication using Paxos

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

volatile and lookup table

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

Fine grained access rights according to SQL-standard with additional protections for files

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

simple password-based access control

More information provided by the system vendor

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