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DBMS > Drizzle vs. Google Cloud Datastore vs. Microsoft Azure Data Explorer vs. SiteWhere

System Properties Comparison Drizzle vs. Google Cloud Datastore vs. Microsoft Azure Data Explorer vs. SiteWhere

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Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSiteWhere  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformFully managed big data interactive analytics platformM2M integration platform for persisting/querying time series data
Primary database modelRelational DBMSDocument storeRelational DBMS infocolumn orientedTime Series DBMS
Secondary database modelsDocument 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
Score4.36
Rank#72  Overall
#12  Document stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.06
Rank#383  Overall
#43  Time Series DBMS
Websitecloud.google.com/­datastoreazure.microsoft.com/­services/­data-explorergithub.com/­sitewhere/­sitewhere
Technical documentationcloud.google.com/­datastore/­docsdocs.microsoft.com/­en-us/­azure/­data-explorersitewhere1.sitewhere.io/­index.html
DeveloperDrizzle project, originally started by Brian AkerGoogleMicrosoftSiteWhere
Initial release2008200820192010
Current release7.2.4, September 2012cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialcommercialOpen Source infoCommon Public Attribution License Version 1.0
Cloud-based only infoOnly available as a cloud servicenoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Java
Server operating systemsFreeBSD
Linux
OS X
hostedhostedLinux
OS X
Windows
Data schemeyesschema-freeFixed schema with schema-less datatypes (dynamic)predefined scheme
Typing infopredefined data types such as float or dateyesyes, details hereyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.noyesno
Secondary indexesyesyesall fields are automatically indexedno
SQL infoSupport of SQLyes infowith proprietary extensionsSQL-like query language (GQL)Kusto Query Language (KQL), SQL subsetno
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
HTTP REST
Supported programming languagesC
C++
Java
PHP
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresnousing Google App EngineYes, possible languages: KQL, Python, R
Triggersno infohooks for callbacks inside the server can be used.Callbacks using the Google Apps Engineyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud serviceSharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using Paxosyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.selectable replication factor infobased on HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud DataflowSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Eventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnono
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.nonono
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Azure Active Directory AuthenticationUsers with fine-grained authorization concept

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More resources
DrizzleGoogle Cloud DatastoreMicrosoft Azure Data ExplorerSiteWhere
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