DBMS > Apache Impala vs. FileMaker vs. Microsoft Azure Data Explorer vs. OrigoDB vs. Yanza

System Properties Comparison Apache Impala vs. FileMaker vs. Microsoft Azure Data Explorer vs. OrigoDB vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Analytic DBMS for Hadoop

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

Fully managed big data interactive analytics platform

A fully ACID in-memory object graph database

Time Series DBMS for IoT Applications

Primary database model

Relational DBMS

column oriented

Document store
Object oriented DBMS

Time Series DBMS

Secondary database models

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

impala.apache.org

www.claris.com/filemaker

azure.microsoft.com/services/data-explorer

origodb.com

yanza.com

Technical documentation

impala.apache.org/impala-docs.html

www.claris.com/resources/documentation

docs.microsoft.com/en-us/azure/data-explorer

origodb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Claris

a subsidiary of Apple

Microsoft

Robert Friberg et al

Yanza

Initial release

2013

1983

2019

2009

under the name LiveDB

2015

Current release

4.1.0, June 2022

19.4.1, November 2021

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

Linux

iOS

client part only
Linux
OS X
Windows

hosted

Linux
Windows

Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

schema-free

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

via plugins

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC
ODBC

Filemaker WebDirect
JDBC
ODBC

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

.NET Client API
HTTP API
LINQ

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

PHP

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

.Net

any language that supports HTTP calls

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Yes, possible languages: KQL, Python, R

yes

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

yes

Domain Events

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

horizontal partitioning

client side managed; servers are not synchronized

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication, warm standby

since Version 14

yes

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

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

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

Write ahead log

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

Azure Active Directory Authentication

Role based authorization

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