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DBMS > 4D vs. CrateDB vs. FileMaker vs. Spark SQL

System Properties Comparison 4D vs. CrateDB vs. FileMaker vs. Spark SQL

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Editorial information provided by DB-Engines
Name4D infoformer name: 4th Dimension  Xexclude from comparisonCrateDB  Xexclude from comparisonFileMaker  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionApplication development environment with integrated database management systemDistributed Database based on LuceneFileMaker is a cross-platform RDBMS that includes a GUI frontend.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSRelational DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.58
Rank#108  Overall
#54  Relational DBMS
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score48.20
Rank#20  Overall
#14  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitewww.4d.comcratedb.comwww.claris.com/­filemakerspark.apache.org/­sql
Technical documentationdeveloper.4d.comcratedb.com/­docswww.claris.com/­resources/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
Developer4D, IncCrateClaris infoa subsidiary of AppleApache Software Foundation
Initial release1984201319832014
Current releasev20, April 202319.4.1, November 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen SourcecommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageJavaScala
Server operating systemsOS X
Windows
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportiOS infoclient part only
Linux
OS X
Windows
Linux
OS X
Windows
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or dateyesyesyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.yesnoyes infoA http query request can return the data in XML formatno
Secondary indexesyesyesyesno
SQL infoSupport of SQLyes infoclose to SQL 92yes, but no triggers and constraints, and PostgreSQL compatibilityyes infovia pluginsSQL-like DML and DDL statements
APIs and other access methodsODBC
RESTful HTTP API infoby using 4D Mobile
SOAP webservices
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
Filemaker WebDirect
JDBC
ODBC
JDBC
ODBC
Supported programming languages4D proprietary IDE
PHP
.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
PHPJava
Python
R
Scala
Server-side scripts infoStored proceduresyesuser defined functions (Javascript)yesno
Triggersyesnoyesno
Partitioning methods infoMethods for storing different data on different nodesnoneShardingnoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationConfigurable replication on table/partition-levelSource-replica replication, warm standby infosince Version 14none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Read-after-write consistency on record level
Foreign keys infoReferential integrityyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno infounique row identifiers can be used for implementing an optimistic concurrency control strategynono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlUsers and groupsrights management via user accountssimple rights management via user accounts and connection to external directory servicesno
More information provided by the system vendor
4D infoformer name: 4th DimensionCrateDBFileMakerSpark SQL
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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4D infoformer name: 4th DimensionCrateDBFileMakerSpark SQL
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