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DBMS > Amazon Redshift vs. etcd vs. Kinetica vs. Spark SQL vs. Splice Machine

System Properties Comparison Amazon Redshift vs. etcd vs. Kinetica vs. Spark SQL vs. Splice Machine

Editorial information provided by DB-Engines
NameAmazon Redshift  Xexclude from comparisonetcd  Xexclude from comparisonKinetica  Xexclude from comparisonSpark SQL  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionLarge scale data warehouse service for use with business intelligence toolsA distributed reliable key-value storeFully vectorized database across both GPUs and CPUsSpark SQL is a component on top of 'Spark Core' for structured data processingOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSKey-value storeRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score17.94
Rank#34  Overall
#21  Relational DBMS
Score7.25
Rank#54  Overall
#5  Key-value stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websiteaws.amazon.com/­redshiftetcd.io
github.com/­etcd-io/­etcd
www.kinetica.comspark.apache.org/­sqlsplicemachine.com
Technical documentationdocs.aws.amazon.com/­redshiftetcd.io/­docs
github.com/­etcd-io/­etcd/­tree/­master/­Documentation
docs.kinetica.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsplicemachine.com/­how-it-works
DeveloperAmazon (based on PostgreSQL)KineticaApache Software FoundationSplice Machine
Initial release2012201220142014
Current release3.4, August 20197.1, August 20213.5.0 ( 2.13), September 20233.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercialOpen Source infoApache 2.0Open Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCGoC, C++ScalaJava
Server operating systemshostedFreeBSD
Linux
Windows infoexperimental
LinuxLinux
OS X
Windows
Linux
OS X
Solaris
Windows
Data schemeyesschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesnoyesyesyes
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.nononono
Secondary indexesrestrictednoyesnoyes
SQL infoSupport of SQLyes infodoes not fully support an SQL-standardnoSQL-like DML and DDL statementsSQL-like DML and DDL statementsyes
APIs and other access methodsJDBC
ODBC
gRPC
JSON over HTTP
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl
C++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions infoin Pythonnouser defined functionsnoyes infoJava
Triggersnoyes, watching key changesyes infotriggers when inserted values for one or more columns fall within a specified rangenoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark CoreShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesUsing Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.Source-replica replicationnoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyes infoinformational only, not enforced by the systemnoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnononoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
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.yesnoyes infoGPU vRAM or System RAMnoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardnoAccess rights for users and roles on table levelnoAccess rights for users, groups and roles according to SQL-standard

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More resources
Amazon RedshiftetcdKineticaSpark SQLSplice Machine
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