DBMS > Apache Impala vs. FileMaker vs. Ignite vs. Microsoft SQL Server vs. RDFox

System Properties Comparison Apache Impala vs. FileMaker vs. Ignite vs. Microsoft SQL Server vs. RDFox

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Microsofts flagship relational DBMS

High performance knowledge graph and semantic reasoning engine

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Graph DBMS
RDF store

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.23
Rank	#308	Overall
	#25	Graph DBMS
	#14	RDF stores

Website

impala.apache.org

www.claris.com/filemaker

ignite.apache.org

www.microsoft.com/en-us/sql-server

www.oxfordsemantic.tech

Technical documentation

impala.apache.org/impala-docs.html

www.claris.com/resources/documentation

apacheignite.readme.io/docs

learn.microsoft.com/en-US/sql/sql-server

docs.oxfordsemantic.tech

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Claris

a subsidiary of Apple

Apache Software Foundation

Microsoft

Oxford Semantic Technologies

Initial release

2013

1983

2015

1989

2017

Current release

4.1.0, June 2022

19.4.1, November 2021

Apache Ignite 2.6

SQL Server 2022, November 2022

6.0, Septermber 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

C++

C++, Java, .Net

C++

Server operating systems

Linux

iOS

client part only
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux
Windows

Linux
macOS
Windows

Data scheme

yes

RDF schemas

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.

yes

A http query request can return the data in XML format

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

via plugins

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

JDBC
ODBC

Filemaker WebDirect
JDBC
ODBC

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

RESTful HTTP API
SPARQL 1.1

Supported programming languages

All languages supporting JDBC/ODBC

PHP

C#
C++
Java
PHP
Python
Ruby
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

yes (compute grid and cache interceptors can be used instead)

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication, warm standby

since Version 14

yes (replicated cache)

yes, but depending on the SQL-Server Edition

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

yes

Transaction concepts

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

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 for users, groups and roles

based on Apache Sentry and Kerberos

simple rights management via user accounts and connection to external directory services

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

Roles, resources, and access types

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