DBMS > Apache Impala vs. Apache Kylin vs. FatDB vs. Warp 10

System Properties Comparison Apache Impala vs. Apache Kylin vs. FatDB vs. Warp 10

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Analytic DBMS for Hadoop

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

Primary database model

Relational DBMS

Document store
Key-value store

Time Series DBMS

Secondary database models

Document store

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	1.14
Rank	#171	Overall
	#77	Relational DBMS

Score	0.06
Rank	#351	Overall
	#35	Time Series DBMS

Website

impala.apache.org

kylin.apache.org

www.warp10.io

Technical documentation

impala.apache.org/impala-docs.html

kylin.apache.org/docs

www.warp10.io/content/02_Getting_started

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation, originally contributed from eBay Inc

FatCloud

SenX

Initial release

2013

2015

2012

2015

Current release

4.1.0, June 2022

3.1.0, July 2020

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

Open Source

Apache License 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

Server operating systems

Linux

Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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

SQL-like DML and DDL statements

ANSI SQL for queries (using Apache Calcite)

Via inetgration in SQL Server

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

via applications

yes

WarpScript

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

Transaction concepts

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

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 for users, groups and roles

based on Apache Sentry and Kerberos

Can implement custom security layer via applications

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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More resources

Recent citations in the news

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9 December 2015, eBay Inc.

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10 August 2021, Blocks & Files

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