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DBMS > Apache Pinot vs. Blazegraph vs. Fujitsu Enterprise Postgres vs. Splice Machine

System Properties Comparison Apache Pinot vs. Blazegraph vs. Fujitsu Enterprise Postgres vs. Splice Machine

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Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonBlazegraph  Xexclude from comparisonFujitsu Enterprise Postgres  Xexclude from comparisonSplice Machine  Xexclude from comparison
Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyHigh-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSGraph DBMS
RDF store
Relational DBMSRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.40
Rank#270  Overall
#125  Relational DBMS
Score0.75
Rank#219  Overall
#19  Graph DBMS
#8  RDF stores
Score0.31
Rank#285  Overall
#129  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitepinot.apache.orgblazegraph.comwww.postgresql.fastware.comsplicemachine.com
Technical documentationdocs.pinot.apache.orgwiki.blazegraph.comwww.postgresql.fastware.com/­product-manualssplicemachine.com/­how-it-works
DeveloperApache Software Foundation and contributorsBlazegraphPostgreSQL Global Development Group, Fujitsu Australia Software TechnologySplice Machine
Initial release201520062014
Current release1.0.0, September 20232.1.5, March 2019Fujitsu Enterprise Postgres 14, January 20223.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoextended commercial license availablecommercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaCJava
Server operating systemsAll OS with a Java JDK11 or higherLinux
OS X
Windows
Linux
Windows
Linux
OS X
Solaris
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes infoRDF literal typesyesyes
Secondary indexesyesyesyes
SQL infoSupport of SQLSQL-like query languageSPARQL is used as query languageyesyes
APIs and other access methodsJDBCJava API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
Native Spark Datasource
ODBC
Supported programming languagesGo
Java
Python
.Net
C
C++
Java
JavaScript
PHP
Python
Ruby
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyesuser defined functionsyes infoJava
Triggersnoyesyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingpartitioning by range, list and by hashShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesSource-replica replicationMulti-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 Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infoRelationships in Graphsyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes
User concepts infoAccess controlSecurity and Authentication via Web Application Container (Tomcat, Jetty)fine grained access rights according to SQL-standardAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
Apache PinotBlazegraphFujitsu Enterprise PostgresSplice Machine
Specific characteristics100% compatible with community PostgreSQL
» more
Competitive advantagesBuilt-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more
Typical application scenariosTransactional payments applications, reporting and mixed workloads.
» more
Market metricsOver 30 years experience in database technology. Over 20 years in Postgres development...
» more
Licensing and pricing modelsCore based licensing
» more

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Apache PinotBlazegraphFujitsu Enterprise PostgresSplice Machine
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