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DBMS > Apache Impala vs. Firebase Realtime Database vs. GridDB vs. Microsoft Azure Table Storage

System Properties Comparison Apache Impala vs. Firebase Realtime Database vs. GridDB vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonGridDB  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Scalable in-memory time series database optimized for IoT and Big DataA Wide Column Store for rapid development using massive semi-structured datasets
Primary database modelRelational DBMSDocument storeTime Series DBMSWide column store
Secondary database modelsDocument storeKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score13.64
Rank#39  Overall
#6  Document stores
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score4.04
Rank#77  Overall
#6  Wide column stores
Websiteimpala.apache.orgfirebase.google.com/­products/­realtime-databasegriddb.netazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationimpala.apache.org/­impala-docs.htmlfirebase.google.com/­docs/­databasedocs.griddb.net
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaGoogle infoacquired by Google 2014Toshiba CorporationMicrosoft
Initial release2013201220132012
Current release4.1.0, June 20225.1, August 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++
Server operating systemsLinuxhostedLinuxhosted
Data schemeyesschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyesyes infonumerical, string, blob, geometry, boolean, timestampyes
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 indexesyesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSQL92, SQL-like TQL (Toshiba Query Language)no
APIs and other access methodsJDBC
ODBC
Android
iOS
JavaScript API
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCJava
JavaScript
Objective-C
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducelimited functionality with using 'rules'nono
TriggersnoCallbacks are triggered when data changesyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate consistency within container, eventual consistency across containersImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyesACID at container leveloptimistic locking
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.noyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosyes, based on authentication and database rulesAccess rights for users can be defined per databaseAccess rights based on private key authentication or shared access signatures
More information provided by the system vendor
Apache ImpalaFirebase Realtime DatabaseGridDBMicrosoft Azure Table Storage
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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More resources
Apache ImpalaFirebase Realtime DatabaseGridDBMicrosoft Azure Table Storage
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