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DBMS > Google BigQuery vs. Google Cloud Datastore vs. Heroic vs. Microsoft SQL Server vs. SAP HANA

System Properties Comparison Google BigQuery vs. Google Cloud Datastore vs. Heroic vs. Microsoft SQL Server vs. SAP HANA

Editorial information provided by DB-Engines
NameGoogle BigQuery  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHeroic  Xexclude from comparisonMicrosoft SQL Server  Xexclude from comparisonSAP HANA  Xexclude from comparison
DescriptionLarge scale data warehouse service with append-only tablesAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchMicrosofts flagship relational DBMSIn-memory, column based data store. Available as appliance or cloud service
Primary database modelRelational DBMSDocument storeTime Series DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
Document store
Graph DBMS infowith SAP Hana, Enterprise Edition
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score45.62
Rank#24  Overall
#16  Relational DBMS
Score4.84
Rank#76  Overall
#11  Document stores
Score0.21
Rank#290  Overall
#26  Time Series DBMS
Score944.81
Rank#3  Overall
#3  Relational DBMS
Score56.92
Rank#21  Overall
#13  Relational DBMS
Websitecloud.google.com/­bigquerycloud.google.com/­datastorespotify.github.io/­heroic/­#!/­indexwww.microsoft.com/­en-us/­sql-serverwww.sap.com/­products/­hana.html
Technical documentationcloud.google.com/­bigquery/­docscloud.google.com/­datastore/­docsspotify.github.io/­heroic/­#!/­docs/­overviewdocs.microsoft.com/­en-US/­sql/­sql-serverhelp.sap.com/­hana
DeveloperGoogleGoogleSpotifyMicrosoftSAP
Initial release20102008201419892010
Current releaseSQL Server 2019, November 20192.0 SPS05 (June 26, 2020), June 2020
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0commercial inforestricted free version is availablecommercial
Cloud-based only infoOnly available as a cloud serviceyesyesnonono infoalso available as a cloud based service
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++
Server operating systemshostedhostedLinux
Windows
Appliance or cloud-service
Data schemeyesschema-freeschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyesyes
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.nononoyesno
Secondary indexesnoyesyes infovia Elasticsearchyesyes
SQL infoSupport of SQLyesSQL-like query language (GQL)noyesyes
APIs and other access methodsRESTful HTTP/JSON APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HQL (Heroic Query Language, a JSON-based language)
HTTP API
ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)
JDBC
ODBC
Supported programming languages.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic
Server-side scripts infoStored proceduresuser defined functions infoin JavaScriptusing Google App EnginenoTransact SQL, .NET languages, R, Python and (with SQL Server 2019) JavaSQLScript, R
TriggersnoCallbacks using the Google Apps Enginenoyesyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingtables can be distributed across several files (horizontal partitioning); sharding through federationyes
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosyesyes, but depending on the SQL-Server Editionyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate 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 ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infoSince BigQuery is designed for querying dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononoyesyes
User concepts infoAccess controlAccess privileges (owner, writer, reader) on dataset, table or view level infoGoogle Cloud Identity & Access Management (IAM)Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights according to SQL-standardyes

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More resources
Google BigQueryGoogle Cloud DatastoreHeroicMicrosoft SQL ServerSAP HANA
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