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DBMS > Badger vs. Ignite vs. Spark SQL vs. Splice Machine vs. UniData,UniVerse

System Properties Comparison Badger vs. Ignite vs. Spark SQL vs. Splice Machine vs. UniData,UniVerse

Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonIgnite  Xexclude from comparisonSpark SQL  Xexclude from comparisonSplice Machine  Xexclude from comparisonUniData,UniVerse  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.Spark 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 SparkMultiValue database and application server with SQL mapping layer and meta database capabilities
Primary database modelKey-value storeKey-value store
Relational DBMS
Relational DBMSRelational DBMSMultivalue DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.14
Rank#331  Overall
#49  Key-value stores
Score3.16
Rank#96  Overall
#15  Key-value stores
#49  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Score3.16
Rank#97  Overall
#2  Multivalue DBMS
Websitegithub.com/­dgraph-io/­badgerignite.apache.orgspark.apache.org/­sqlsplicemachine.comwww.rocketsoftware.com/­products/­rocket-multivalue-application-development-platform/­rocket-unidata
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerapacheignite.readme.io/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsplicemachine.com/­how-it-worksdocs.rocketsoftware.com/­bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata
DeveloperDGraph LabsApache Software FoundationApache Software FoundationSplice MachineRocket Software
Initial release20172015201420141985
Current releaseApache Ignite 2.63.5.0 ( 2.13), September 20233.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache 2.0Open Source infoApache 2.0Open Source infoAGPL 3.0, commercial license availablecommercial
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC++, Java, .NetScalaJavaC
Server operating systemsBSD
Linux
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Linux
OS X
Solaris
Windows
AIX
HP-UX
Linux
Solaris
Windows
Data schemeschema-freeyesyesyesschema-free
Typing infopredefined data types such as float or datenoyesyesyesoptional
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.noyesno
Secondary indexesnoyesnoyesyes
SQL infoSupport of SQLnoANSI-99 for query and DML statements, subset of DDLSQL-like DML and DDL statementsyesyes
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Java API infoJPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API
Supported programming languagesGoC#
C++
Java
PHP
Python
Ruby
Scala
Java
Python
R
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
.Net
Basic infoU2 Basic
C
Java
Server-side scripts infoStored proceduresnoyes (compute grid and cache interceptors can be used instead)noyes infoJavayes
Triggersnoyes (cache interceptors and events)noyesyes infoU2 Basic
Partitioning methods infoMethods for storing different data on different nodesnoneShardingyes, utilizing Spark CoreShared Nothhing Auto-Sharding, Columnar Partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyes (replicated cache)noneMulti-source replication
Source-replica replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)Yes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACIDACID infoconfigurable
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)yes
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.noyesnoyes
User concepts infoAccess controlnoSecurity Hooks for custom implementationsnoAccess rights for users, groups and roles according to SQL-standardAccess rights according to SQL-standard and operating system based

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More resources
BadgerIgniteSpark SQLSplice MachineUniData,UniVerse
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