DBMS > FileMaker vs. GeoMesa vs. Google BigQuery vs. Microsoft Azure Data Explorer

System Properties Comparison FileMaker vs. GeoMesa vs. Google BigQuery vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines

Name

Description

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

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Large scale data warehouse service with append-only tables

Fully managed big data interactive analytics platform

Primary database model

Relational DBMS

Spatial DBMS

Relational DBMS

column oriented

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	47.91
Rank	#20	Overall
	#14	Relational DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Website

www.claris.com/filemaker

www.geomesa.org

cloud.google.com/bigquery

azure.microsoft.com/services/data-explorer

Technical documentation

www.claris.com/resources/documentation

www.geomesa.org/documentation/stable/user/index.html

cloud.google.com/bigquery/docs

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

Developer

Claris

a subsidiary of Apple

CCRi and others

Google

Microsoft

Initial release

1983

2014

2010

2019

Current release

19.4.1, November 2021

5.0.0, May 2024

cloud service with continuous releases

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

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

Scala

Server operating systems

iOS

client part only
Linux
OS X
Windows

hosted

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

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

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

all fields are automatically indexed

SQL

Support of SQL

yes

via plugins

yes

Kusto Query Language (KQL), SQL subset

APIs and other access methods

Filemaker WebDirect
JDBC
ODBC

RESTful HTTP/JSON API

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

Supported programming languages

PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Yes, possible languages: KQL, Python, R

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

none

depending on storage layer

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication, warm standby

since Version 14

depending on storage layer

yes

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying 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.

depending on storage layer

User concepts

Access control

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

yes

depending on the DBMS used for storage

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

Google Cloud Identity & Access Management (IAM)

Azure Active Directory Authentication

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