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DBMS > Apache Impala vs. Dragonfly vs. GridDB vs. Microsoft Azure Synapse Analytics

System Properties Comparison Apache Impala vs. Dragonfly vs. GridDB vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDragonfly  Xexclude from comparisonGridDB  Xexclude from comparisonMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopA drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instanceScalable in-memory time series database optimized for IoT and Big DataElastic, large scale data warehouse service leveraging the broad eco-system of SQL Server
Primary database modelRelational DBMSKey-value storeTime Series DBMSRelational DBMS
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
Score0.49
Rank#261  Overall
#38  Key-value stores
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score19.93
Rank#31  Overall
#19  Relational DBMS
Websiteimpala.apache.orggithub.com/­dragonflydb/­dragonfly
www.dragonflydb.io
griddb.netazure.microsoft.com/­services/­synapse-analytics
Technical documentationimpala.apache.org/­impala-docs.htmlwww.dragonflydb.io/­docsdocs.griddb.netdocs.microsoft.com/­azure/­synapse-analytics
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDragonflyDB team and community contributorsToshiba CorporationMicrosoft
Initial release2013202320132016
Current release4.1.0, June 20221.0, March 20235.1, August 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoBSL 1.1Open 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 servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++C++C++
Server operating systemsLinuxLinuxLinuxhosted
Data schemeyesscheme-freeyesyes
Typing infopredefined data types such as float or dateyesstrings, hashes, lists, sets, sorted sets, bit arraysyes 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 indexesyesnoyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSQL92, SQL-like TQL (Toshiba Query Language)yes
APIs and other access methodsJDBC
ODBC
Proprietary protocol infoRESP - REdis Serialization ProtocolJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C#
Java
PHP
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceLuanoTransact SQL
Triggersnopublish/subscribe channels provide some trigger functionalityyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding, horizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationSource-replica replicationyes
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 ConsistencyImmediate consistency within container, eventual consistency across containersImmediate Consistency
Foreign keys infoReferential integritynononono infodocs.microsoft.com/­en-us/­azure/­synapse-analytics/­sql-data-warehouse/­sql-data-warehouse-table-constraints
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of command blocks and scriptsACID at container levelACID
Concurrency infoSupport for concurrent manipulation of datayesyes, strict serializability by the serveryesyes
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 KerberosPassword-based authenticationAccess rights for users can be defined per databaseyes
More information provided by the system vendor
Apache ImpalaDragonflyGridDBMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse
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 ImpalaDragonflyGridDBMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse
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