DBMS > FileMaker vs. Google BigQuery vs. LevelDB vs. Microsoft Access vs. Solr

System Properties Comparison FileMaker vs. Google BigQuery vs. LevelDB vs. Microsoft Access vs. Solr

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

Spatial DBMS

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Trend Chart

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.claris.com/filemaker

cloud.google.com/bigquery

github.com/google/leveldb

www.microsoft.com/en-us/microsoft-365/access

solr.apache.org

Technical documentation

www.claris.com/resources/documentation

cloud.google.com/bigquery/docs

github.com/google/leveldb/blob/main/doc/index.md

developer.microsoft.com/en-us/access

solr.apache.org/resources.html

Developer

Claris

a subsidiary of Apple

Google

Microsoft

Apache Software Foundation

Initial release

1983

2010

2011

1992

2006

Current release

19.4.1, November 2021

1.23, February 2021

1902 (16.0.11328.20222), March 2019

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

BSD

commercial

Bundled with Microsoft Office

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

iOS

client part only
Linux
OS X
Windows

hosted

Illumos
Linux
OS X
Windows

Windows

Not a real database server, but making use of DLLs

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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 search fields are automatically indexed

SQL

Support of SQL

yes

via plugins

yes

but not compliant to any SQL standard

Solr Parallel SQL Interface

APIs and other access methods

Filemaker WebDirect
JDBC
ODBC

RESTful HTTP/JSON API

ADO.NET
DAO
ODBC
OLE DB

Java API
RESTful HTTP/JSON API

Supported programming languages

PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

yes

since Access 2010 using the ACE-engine

Java plugins

Triggers

yes

since Access 2010 using the ACE-engine

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication, warm standby

since Version 14

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

ACID

but no files for transaction logging

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

but no files for transaction logging

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

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

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

Google Cloud Identity & Access Management (IAM)

a simple user-level security was built in till version Access 2003

yes

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