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DBMS > Databricks vs. Faircom DB vs. IBM Db2 Event Store vs. RDF4J

System Properties Comparison Databricks vs. Faircom DB vs. IBM Db2 Event Store vs. RDF4J

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonIBM Db2 Event Store  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
DescriptionThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.Distributed Event Store optimized for Internet of Things use casesRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database modelDocument store
Relational DBMS
Key-value store
Relational DBMS
Event Store
Time Series DBMS
RDF store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.29
Rank#304  Overall
#43  Key-value stores
#136  Relational DBMS
Score0.27
Rank#309  Overall
#2  Event Stores
#28  Time Series DBMS
Score0.74
Rank#222  Overall
#9  RDF stores
Websitewww.databricks.comwww.faircom.com/­products/­faircom-dbwww.ibm.com/­products/­db2-event-storerdf4j.org
Technical documentationdocs.databricks.comdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmlwww.ibm.com/­docs/­en/­db2-event-storerdf4j.org/­documentation
DeveloperDatabricksFairCom CorporationIBMSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release2013197920172004
Current releaseV12, November 20202.0
License infoCommercial or Open Sourcecommercialcommercial infoRestricted, free version availablecommercial infofree developer edition availableOpen Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageANSI C, C++C and C++Java
Server operating systemshostedAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
Linux infoLinux, macOS, Windows for the developer additionLinux
OS X
Unix
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema free, schema optional, schema required, partial schema,yesyes infoRDF Schemas
Typing infopredefined data types such as float or dateyes, ANSI SQL Types, JSON, typed binary structuresyesyes
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.yesnono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLwith Databricks SQLyes, ANSI SQL with proprietary extensionsyes infothrough the embedded Spark runtimeno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Supported programming languagesPython
R
Scala
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
Java
PHP
Python
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes info.Net, JavaScript, C/C++yesyes
Triggersyesnoyes
Partitioning methods infoMethods for storing different data on different nodesFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).Active-active shard replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Eventual Consistency
Foreign keys infoReferential integrityyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDtunable from ACID to Eventually ConsistentnoACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesNo - written data is immutableyes
Durability infoSupport for making data persistentyesYes, tunable from durable to delayed durability to in-memoryYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyes infoin-memory storage is supported as well
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes
User concepts infoAccess controlFine grained access rights according to SQL-standard with additional protections for filesfine grained access rights according to SQL-standardno
More information provided by the system vendor
DatabricksFaircom DB infoformerly c-treeACEIBM Db2 Event StoreRDF4J infoformerly known as Sesame
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksFaircom DB infoformerly c-treeACEIBM Db2 Event StoreRDF4J infoformerly known as Sesame
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