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DBMS > Apache Impala vs. Blazegraph vs. Datomic vs. Google Cloud Bigtable vs. H2GIS

System Properties Comparison Apache Impala vs. Blazegraph vs. Datomic vs. Google Cloud Bigtable vs. H2GIS

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonBlazegraph  Xexclude from comparisonDatomic  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonH2GIS  Xexclude from comparison
Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.
DescriptionAnalytic DBMS for HadoopHigh-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Spatial extension of H2
Primary database modelRelational DBMSGraph DBMS
RDF store
Relational DBMSKey-value store
Wide column store
Spatial DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score0.75
Rank#219  Overall
#19  Graph DBMS
#8  RDF stores
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score0.00
Rank#383  Overall
#7  Spatial DBMS
Websiteimpala.apache.orgblazegraph.comwww.datomic.comcloud.google.com/­bigtablewww.h2gis.org
Technical documentationimpala.apache.org/­impala-docs.htmlwiki.blazegraph.comdocs.datomic.comcloud.google.com/­bigtable/­docswww.h2gis.org/­docs/­home
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaBlazegraphCognitectGoogleCNRS
Initial release20132006201220152013
Current release4.1.0, June 20222.1.5, March 20191.0.6735, June 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoextended commercial license availablecommercial infolimited edition freecommercialOpen Source infoLGPL 3.0
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaJava, ClojureJava
Server operating systemsLinuxLinux
OS X
Windows
All OS with a Java VMhosted
Data schemeyesschema-freeyesschema-freeyes
Typing infopredefined data types such as float or dateyesyes infoRDF literal typesyesnoyes
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 indexesyesyesyesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSPARQL is used as query languagenonoyes
APIs and other access methodsJDBC
ODBC
Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3
RESTful HTTP APIgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Supported programming languagesAll languages supporting JDBC/ODBC.Net
C
C++
Java
JavaScript
PHP
Python
Ruby
Clojure
Java
C#
C++
Go
Java
JavaScript (Node.js)
Python
Java
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyesyes infoTransaction Functionsnoyes infobased on H2
TriggersnonoBy using transaction functionsnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnone infoBut extensive use of caching in the application peersShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesnone infoBut extensive use of caching in the application peersInternal replication in Colossus, and regional replication between two clusters in different zonesyes infobased on H2
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenonoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency
Foreign keys infoReferential integritynoyes infoRelationships in Graphsnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDAtomic single-row operationsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes inforecommended only for testing and developmentnoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosSecurity and Authentication via Web Application Container (Tomcat, Jetty)noAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yes infobased on H2

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More resources
Apache ImpalaBlazegraphDatomicGoogle Cloud BigtableH2GIS
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