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DBMS > Apache Kylin vs. Badger vs. Citus vs. Databricks vs. Faircom DB

System Properties Comparison Apache Kylin vs. Badger vs. Citus vs. Databricks vs. Faircom DB

Editorial information provided by DB-Engines
NameApache Kylin  Xexclude from comparisonBadger  Xexclude from comparisonCitus  Xexclude from comparisonDatabricks  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparison
DescriptionA distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stackAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLThe 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.
Primary database modelRelational DBMSKey-value storeRelational DBMSDocument store
Relational DBMS
Key-value store
Relational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.25
Rank#170  Overall
#77  Relational DBMS
Score0.22
Rank#320  Overall
#47  Key-value stores
Score2.15
Rank#117  Overall
#56  Relational DBMS
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.29
Rank#304  Overall
#43  Key-value stores
#136  Relational DBMS
Websitekylin.apache.orggithub.com/­dgraph-io/­badgerwww.citusdata.comwww.databricks.comwww.faircom.com/­products/­faircom-db
Technical documentationkylin.apache.org/­docsgodoc.org/­github.com/­dgraph-io/­badgerdocs.citusdata.comdocs.databricks.comdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.html
DeveloperApache Software Foundation, originally contributed from eBay IncDGraph LabsDatabricksFairCom Corporation
Initial release20152017201020131979
Current release3.1.0, July 20208.1, December 2018V12, November 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache 2.0Open Source infoAGPL, commercial license also availablecommercialcommercial infoRestricted, free version available
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaGoCANSI C, C++
Server operating systemsLinuxBSD
Linux
OS X
Solaris
Windows
LinuxhostedAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
Data schemeyesschema-freeyesFlexible Schema (defined schema, partial schema, schema free)schema free, schema optional, schema required, partial schema,
Typing infopredefined data types such as float or dateyesnoyesyes, ANSI SQL Types, JSON, typed binary structures
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.nonoyes infospecific XML type available, but no XML query functionalityyesno
Secondary indexesyesnoyesyesyes
SQL infoSupport of SQLANSI SQL for queries (using Apache Calcite)noyes infostandard, with numerous extensionswith Databricks SQLyes, ANSI SQL with proprietary extensions
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
RESTful HTTP API
ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
Supported programming languagesGo.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Python
R
Scala
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
Server-side scripts infoStored proceduresnouser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.user defined functions and aggregatesyes info.Net, JavaScript, C/C++
Triggersnoyesyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnoneSource-replica replication infoother methods possible by using 3rd party extensionsyesyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencynoneImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Foreign keys infoReferential integritynoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDtunable from ACID to Eventually Consistent
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesYes, tunable from durable to delayed durability to in-memory
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonononoyes
User concepts infoAccess controlnofine grained access rights according to SQL-standardFine grained access rights according to SQL-standard with additional protections for files
More information provided by the system vendor
Apache KylinBadgerCitusDatabricksFaircom DB infoformerly c-treeACE
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache KylinBadgerCitusDatabricksFaircom DB infoformerly c-treeACE
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