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DBMS > Apache Impala vs. Fujitsu Enterprise Postgres vs. HugeGraph vs. Realm

System Properties Comparison Apache Impala vs. Fujitsu Enterprise Postgres vs. HugeGraph vs. Realm

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonFujitsu Enterprise Postgres  Xexclude from comparisonHugeGraph  Xexclude from comparisonRealm  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopEnterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.A fast-speed and highly-scalable Graph DBMSA DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data
Primary database modelRelational DBMSRelational DBMSGraph DBMSDocument store
Secondary database modelsDocument storeDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score0.31
Rank#285  Overall
#129  Relational DBMS
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score7.60
Rank#52  Overall
#9  Document stores
Websiteimpala.apache.orgwww.postgresql.fastware.comgithub.com/­hugegraph
hugegraph.apache.org
realm.io
Technical documentationimpala.apache.org/­impala-docs.htmlwww.postgresql.fastware.com/­product-manualshugegraph.apache.org/­docsrealm.io/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyBaiduRealm, acquired by MongoDB in May 2019
Initial release201320182014
Current release4.1.0, June 2022Fujitsu Enterprise Postgres 14, January 20220.9
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoApache Version 2.0Open Source
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++CJava
Server operating systemsLinuxLinux
Windows
Linux
macOS
Unix
Android
Backend: server-less
iOS
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nonono
Secondary indexesyesyesyes infoalso supports composite index and range indexyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyesnono
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Java API
RESTful HTTP API
TinkerPop Gremlin
Supported programming languagesAll languages supporting JDBC/ODBC.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Groovy
Java
Python
.Net
Java infowith Android only
Objective-C
React Native
Swift
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functionsasynchronous Gremlin script jobsno inforuns within the applications so server-side scripts are unnecessary
Triggersnoyesnoyes infoChange Listeners
Partitioning methods infoMethods for storing different data on different nodesShardingpartitioning by range, list and by hashyes infodepending on used storage backend, e.g. Cassandra and HBasenone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationyes infodepending on used storage backend, e.g. Cassandra and HBasenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenovia hugegraph-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyes infoedges in graphno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes infoIn-Memory realm
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosfine grained access rights according to SQL-standardUsers, roles and permissionsyes
More information provided by the system vendor
Apache ImpalaFujitsu Enterprise PostgresHugeGraphRealm
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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More resources
Apache ImpalaFujitsu Enterprise PostgresHugeGraphRealm
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