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DBMS > CrateDB vs. GeoMesa vs. Google Cloud Datastore vs. Microsoft Azure AI Search

System Properties Comparison CrateDB vs. GeoMesa vs. Google Cloud Datastore vs. Microsoft Azure AI Search

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonGeoMesa  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparison
DescriptionDistributed Database based on LuceneGeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformSearch-as-a-service for web and mobile app development
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Spatial DBMSDocument storeSearch engine
Secondary database modelsRelational DBMSVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score0.78
Rank#213  Overall
#4  Spatial DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score5.59
Rank#63  Overall
#7  Search engines
Websitecratedb.comwww.geomesa.orgcloud.google.com/­datastoreazure.microsoft.com/­en-us/­services/­search
Technical documentationcratedb.com/­docswww.geomesa.org/­documentation/­stable/­user/­index.htmlcloud.google.com/­datastore/­docslearn.microsoft.com/­en-us/­azure/­search
DeveloperCrateCCRi and othersGoogleMicrosoft
Initial release2013201420082015
Current release5.0.0, May 2024V1
License infoCommercial or Open SourceOpen SourceOpen Source infoApache License 2.0commercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageJavaScala
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthostedhosted
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyes, details hereyes
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 indexesyesyesyesyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitynoSQL-like query language (GQL)no
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP API
Supported programming languages.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
Java
JavaScript
Python
Server-side scripts infoStored proceduresuser defined functions (Javascript)nousing Google App Engineno
TriggersnonoCallbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodesShardingdepending on storage layerShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-leveldepending on storage layerMulti-source replication using Paxosyes infoImplicit feature of the cloud service
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
depending on storage layerImmediate 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.Immediate Consistency
Foreign keys infoReferential integritynonoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infounique row identifiers can be used for implementing an optimistic concurrency control strategynoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsno
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.nodepending on storage layernono
User concepts infoAccess controlrights management via user accountsyes infodepending on the DBMS used for storageAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yes infousing Azure authentication
More information provided by the system vendor
CrateDBGeoMesaGoogle Cloud DatastoreMicrosoft Azure AI Search
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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More resources
CrateDBGeoMesaGoogle Cloud DatastoreMicrosoft Azure AI Search
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