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DBMS > Databricks vs. Drizzle vs. EsgynDB vs. Faircom EDGE vs. Tkrzw

System Properties Comparison Databricks vs. Drizzle vs. EsgynDB vs. Faircom EDGE vs. Tkrzw

Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonDrizzle  Xexclude from comparisonEsgynDB  Xexclude from comparisonFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
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.MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Enterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionFairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environmentsA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelDocument store
Relational DBMS
Relational DBMSRelational DBMSKey-value store
Relational DBMS
Key-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score0.02
Rank#368  Overall
#54  Key-value stores
#156  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitewww.databricks.comwww.esgyn.cnwww.faircom.com/­products/­faircom-edgedbmx.net/­tkrzw
Technical documentationdocs.databricks.comdocs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html
DeveloperDatabricksDrizzle project, originally started by Brian AkerEsgynFairCom CorporationMikio Hirabayashi
Initial release20132008201519792020
Current release7.2.4, September 2012V3, October 20200.9.3, August 2020
License infoCommercial or Open SourcecommercialOpen Source infoGNU GPLcommercialcommercial infoRestricted, free version availableOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++, JavaANSI C, C++C++
Server operating systemshostedFreeBSD
Linux
OS X
LinuxAndroid
Linux infoARM, x86
Raspbian
Windows
Linux
macOS
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesFlexible Schema (defined schema, partial schema, schema free)schema-free
Typing infopredefined data types such as float or dateyesyesyes, ANSI Standard SQL Typesno
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.yesnoyesno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLwith Databricks SQLyes infowith proprietary extensionsyesyes infoANSI SQL queriesno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBCADO.NET
JDBC
ODBC
ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
Supported programming languagesPython
R
Scala
C
C++
Java
PHP
All languages supporting JDBC/ODBC/ADO.NetC
C#
C++
Java
JavaScript
PHP
Python
VB.Net
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions and aggregatesnoJava Stored Proceduresyes info.Net, JavaScript, C/C++no
Triggersno infohooks for callbacks inside the server can be used.noyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingFile partitioning infoCustomizable business rules for partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication
Source-replica replication
Multi-source replication between multi datacentersyes infoSynchronous and asynchronous realtime replication based on transaction logsnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Tunable Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesyesyes infowhen using SQLno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infoacross SQL and NoSQLyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes infousing specific database classes
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPfine grained access rights according to SQL-standardFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.no
More information provided by the system vendor
DatabricksDrizzleEsgynDBFaircom EDGE infoformerly c-treeEDGETkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksDrizzleEsgynDBFaircom EDGE infoformerly c-treeEDGETkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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