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DBMS > Apache Impala vs. Datastax Enterprise vs. HugeGraph vs. MonetDB

System Properties Comparison Apache Impala vs. Datastax Enterprise vs. HugeGraph vs. MonetDB

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatastax Enterprise  Xexclude from comparisonHugeGraph  Xexclude from comparisonMonetDB  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopDataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.A fast-speed and highly-scalable Graph DBMSA relational database management system that stores data in columns
Primary database modelRelational DBMSWide column storeGraph DBMSRelational DBMS
Secondary database modelsDocument storeDocument store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS
Document store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score5.80
Rank#60  Overall
#4  Wide column stores
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score1.72
Rank#145  Overall
#67  Relational DBMS
Websiteimpala.apache.orgwww.datastax.com/­products/­datastax-enterprisegithub.com/­hugegraph
hugegraph.apache.org
www.monetdb.org
Technical documentationimpala.apache.org/­impala-docs.htmldocs.datastax.comhugegraph.apache.org/­docswww.monetdb.org/­Documentation
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDataStaxBaiduMonetDB BV
Initial release2013201120182004
Current release4.1.0, June 20226.8, April 20200.9Dec2023 (11.49), December 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoApache Version 2.0Open Source infoMozilla Public License 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaJavaC
Server operating systemsLinuxLinux
OS X
Linux
macOS
Unix
FreeBSD
Linux
OS X
Solaris
Windows
Data schemeyesschema-freeyesyes
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 statementsSQL-like DML and DDL statements (CQL); Spark SQLnoyes infoSQL 2003 with some extensions
APIs and other access methodsJDBC
ODBC
Proprietary protocol infoCQL (Cassandra Query Language)
TinkerPop Gremlin infowith DSE Graph
Java API
RESTful HTTP API
TinkerPop Gremlin
JDBC
native C library infoMAPI library (MonetDB application programming interface)
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Groovy
Java
Python
C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoasynchronous Gremlin script jobsyes, in SQL, C, R
Triggersnoyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infono "single point of failure"yes infodepending on used storage backend, e.g. Cassandra and HBaseSharding via remote tables
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorconfigurable replication factor, datacenter aware, advanced replication for edge computingyes infodepending on used storage backend, e.g. Cassandra and HBasenone infoSource-replica replication available in experimental status
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyesvia hugegraph-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency
Tunable Consistency infoconsistency level can be individually decided with each write operation
Eventual Consistency
Foreign keys infoReferential integritynonoyes infoedges in graphyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infoAtomicity and isolation are supported for single operationsACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users can be defined per objectUsers, roles and permissionsfine grained access rights according to SQL-standard
More information provided by the system vendor
Apache ImpalaDatastax EnterpriseHugeGraphMonetDB
Specific characteristicsDataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more
Competitive advantagesSupporting the following application requirements: Zero downtime - Built on Apache...
» more
Typical application scenariosApplications that must be massively and linearly scalable with 100% uptime and able...
» more
Key customersCapital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
» more
Market metricsAmong the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
» more
Licensing and pricing modelsAnnual subscription
» more

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Apache ImpalaDatastax EnterpriseHugeGraphMonetDB
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