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DBMS > Greenplum vs. GridGain vs. Microsoft Azure Data Explorer vs. RisingWave

System Properties Comparison Greenplum vs. GridGain vs. Microsoft Azure Data Explorer vs. RisingWave

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Editorial information provided by DB-Engines
NameGreenplum  Xexclude from comparisonGridGain  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonRisingWave  Xexclude from comparison
DescriptionAnalytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database infoA logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.GridGain is an in-memory computing platform, built on Apache IgniteFully managed big data interactive analytics platformA distributed RDBMS for stream processing, wire-compatible with PostgreSQL
Primary database modelRelational DBMSKey-value store
Relational DBMS
Relational DBMS infocolumn orientedRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
Document store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score8.08
Rank#48  Overall
#31  Relational DBMS
Score1.55
Rank#150  Overall
#26  Key-value stores
#70  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.64
Rank#238  Overall
#110  Relational DBMS
Websitegreenplum.orgwww.gridgain.comazure.microsoft.com/­services/­data-explorerwww.risingwave.com/­database
Technical documentationdocs.greenplum.orgwww.gridgain.com/­docs/­index.htmldocs.microsoft.com/­en-us/­azure/­data-explorerdocs.risingwave.com/­docs/­current/­intro
DeveloperPivotal Software Inc.GridGain Systems, Inc.MicrosoftRisingWave Labs
Initial release2005200720192022
Current release7.0.0, September 2023GridGain 8.5.1cloud service with continuous releases1.2, September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, C++, .NetRust
Server operating systemsLinuxLinux
OS X
Solaris
Windows
hostedDocker
Linux
macOS
Data schemeyesyesFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesStandard SQL-types and JSON
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.yes infosince Version 4.2yesyesno
Secondary indexesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLyesANSI-99 for query and DML statements, subset of DDLKusto Query Language (KQL), SQL subsetyes
APIs and other access methodsJDBC
ODBC
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
PostgreSQL wire protocol
Supported programming languagesC
Java
Perl
Python
R
C#
C++
Java
PHP
Python
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyesyes (compute grid and cache interceptors can be used instead)Yes, possible languages: KQL, Python, RUDFs in Python or Java
Triggersyesyes (cache interceptors and events)yes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
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 nodesSource-replica replicationyes (replicated cache)yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes (compute grid and hadoop accelerator)Spark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.noyesnoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardSecurity Hooks for custom implementationsAzure Active Directory AuthenticationUsers and Roles

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More resources
GreenplumGridGainMicrosoft Azure Data ExplorerRisingWave
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