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DBMS > Databricks vs. Faircom DB vs. PlanetScale vs. Splice Machine

System Properties Comparison Databricks vs. Faircom DB vs. PlanetScale vs. Splice Machine

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonPlanetScale  Xexclude from comparisonSplice Machine  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.Scalable, distributed, serverless MySQL database platform built on top of VitessOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument store
Relational DBMS
Key-value store
Relational DBMS
Relational DBMSRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
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
Score1.49
Rank#155  Overall
#72  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitewww.databricks.comwww.faircom.com/­products/­faircom-dbplanetscale.comsplicemachine.com
Technical documentationdocs.databricks.comdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmlplanetscale.com/­docssplicemachine.com/­how-it-works
DeveloperDatabricksFairCom CorporationPlanetScaleSplice Machine
Initial release2013197920202014
Current releaseV12, November 20203.1, March 2021
License infoCommercial or Open Sourcecommercialcommercial infoRestricted, free version availablecommercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageANSI C, C++GoJava
Server operating systemshostedAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
Docker
Linux
macOS
Linux
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema free, schema optional, schema required, partial schema,yesyes
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.yesno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLwith Databricks SQLyes, ANSI SQL with proprietary extensionsyes infowith proprietary extensionsyes
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
JDBC
MySQL protocol
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languagesPython
R
Scala
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes info.Net, JavaScript, C/C++yes infoproprietary syntaxyes infoJava
Triggersyesyesyes
Partitioning methods infoMethods for storing different data on different nodesFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningShardingShared Nothhing Auto-Sharding, Columnar Partitioning
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).Multi-source replication
Source-replica replication
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Eventual Consistency across shards
Immediate Consistency within a shard
Immediate Consistency
Foreign keys infoReferential integrityyesyes infonot for MyISAM storage engineyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDtunable from ACID to Eventually ConsistentACID at shard levelACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes infotable locks or row locks depending on storage engineyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesYes, tunable from durable to delayed durability to in-memoryyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesyes
User concepts infoAccess controlFine grained access rights according to SQL-standard with additional protections for filesUsers with fine-grained authorization concept infono user groups or rolesAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
DatabricksFaircom DB infoformerly c-treeACEPlanetScaleSplice Machine
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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DatabricksFaircom DB infoformerly c-treeACEPlanetScaleSplice Machine
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