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DBMS > Google BigQuery vs. JaguarDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage

System Properties Comparison Google BigQuery vs. JaguarDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines
NameGoogle BigQuery  Xexclude from comparisonJaguarDB  Xexclude from comparisonMicrosoft Azure Cosmos DB infoformer name was Azure DocumentDB  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparison
DescriptionLarge scale data warehouse service with append-only tablesPerformant, highly scalable DBMS for AI and IoT applicationsGlobally distributed, horizontally scalable, multi-model database serviceA Wide Column Store for rapid development using massive semi-structured datasets
Primary database modelRelational DBMSKey-value storeDocument store
Graph DBMS
Key-value store
Wide column store
Wide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score33.32
Rank#24  Overall
#15  Relational DBMS
Score0.00
Rank#340  Overall
#61  Key-value stores
Score31.67
Rank#25  Overall
#3  Document stores
#2  Graph DBMS
#3  Key-value stores
#3  Wide column stores
Score5.77
Rank#66  Overall
#5  Wide column stores
Websitecloud.google.com/­bigquerywww.datajaguar.comazure.microsoft.com/­services/­cosmos-dbazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationcloud.google.com/­bigquery/­docswww.datajaguar.com/­support.phpdocs.microsoft.com/­en-us/­azure/­cosmos-db
DeveloperGoogleDataJaguar, Inc.MicrosoftMicrosoft
Initial release2010201520142012
Current release2.9.4 September 2018
License infoCommercial or Open SourcecommercialOpen Source infoGPL V3.0commercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesnoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ infothe server part. Clients available in other languages
Server operating systemshostedLinuxhostedhosted
Data schemeyesyesschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesyes infoJSON 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.nonono
Secondary indexesnoyesyes infoAll properties auto-indexed by defaultno
SQL infoSupport of SQLyesA subset of ANSI SQL is implemented infobut no views, foreign keys, triggersSQL-like query languageno
APIs and other access methodsRESTful HTTP/JSON APIJDBC
ODBC
DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API
RESTful HTTP API
Supported programming languages.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby
C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala
.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions infoin JavaScriptnoJavaScriptno
TriggersnonoJavaScriptno
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding infoImplicit feature of the cloud serviceSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationyes infoImplicit feature of the cloud serviceyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonowith Hadoop integration infoIntegration with Hadoop/HDInsight on Azure*no
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyBounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency infoConsistency level configurable on request level
Session Consistency
Immediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infoSince BigQuery is designed for querying datanoMulti-item ACID transactions with snapshot isolation within a partitionoptimistic 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.nonono
User concepts infoAccess controlAccess privileges (owner, writer, reader) for whole datasets, not for individual tables infoGoogle Cloud Identity & Access Management (IAM)rights management via user accountsAccess rights can be defined down to the item levelAccess rights based on private key authentication or shared access signatures
More information provided by the system vendor
Google BigQueryJaguarDBMicrosoft Azure Cosmos DB infoformer name was Azure DocumentDBMicrosoft Azure Table Storage
Specific characteristicsSQL support, group by, order by, join
» more
Competitive advantagesExtremely easy to scale-out (in seconds) 100,000 times faster
» more
Typical application scenariosIoT data, time series data, AI data. Any type of numerical data.
» more
Key customersQuantea, 2PiDay, Synqy, SinoLogistics, IIVI
» more
Market metricsAdvertising awareness, installed base, brand image, market share
» more
Licensing and pricing modelsFree-trial license, per-node license, and subscription license.
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Google BigQueryJaguarDBMicrosoft Azure Cosmos DB infoformer name was Azure DocumentDBMicrosoft Azure Table Storage
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