DBMS > Google Cloud Datastore vs. Microsoft SQL Server vs. RavenDB
System Properties Comparison Google Cloud Datastore vs. Microsoft SQL Server vs. RavenDB
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|Editorial information provided by DB-Engines
|Google Cloud Datastore Xexclude from comparison
|Microsoft SQL Server Xexclude from comparison
|RavenDB Xexclude from comparison
|Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform
|Microsofts flagship relational DBMS
|Open Source Operational and Transactional Enterprise NoSQL Document Database
|Primary database model
|Secondary database models
Time Series DBMS
|SQL Server 2022, November 2022
|5.4, July 2022
|License Commercial or Open Source
|commercial restricted free version is available
|Open Source AGPL version 3, commercial license available
|Cloud-based only Only available as a cloud service
|DBaaS offerings (sponsored links) Database as a Service
Providers of DBaaS offerings, please contact us to be listed.
|Server operating systems
|Typing predefined data types such as float or date
|yes, details here
|XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.
|SQL Support of SQL
|SQL-like query language (GQL)
|SQL-like query language (RQL)
|APIs and other access methods
|gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Tabular Data Stream (TDS)
|.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API
|Supported programming languages
|Server-side scripts Stored procedures
|using Google App Engine
|Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java
|Callbacks using the Google Apps Engine
|Partitioning methods Methods for storing different data on different nodes
|tables can be distributed across several files (horizontal partitioning); sharding through federation
|Replication methods Methods for redundantly storing data on multiple nodes
|Multi-source replication using Paxos
|yes, but depending on the SQL-Server Edition
|MapReduce Offers an API for user-defined Map/Reduce methods
|yes using Google Cloud Dataflow
|Consistency concepts Methods to ensure consistency in a distributed system
|Immediate Consistency or Eventual Consistency depending on type of query and configuration Strong 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.
|Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.
|Foreign keys Referential integrity
|yes via ReferenceProperties or Ancestor paths
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data
|ACID Serializable Isolation within Transactions, Read Committed outside of Transactions
|ACID, Cluster-wide transaction available
|Concurrency Support for concurrent manipulation of data
|Durability Support for making data persistent
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.
|User concepts Access control
|Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)
|fine grained access rights according to SQL-standard
|Authorization levels configured per client per database
More information provided by the system vendor
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|Related products and services
|DBHawk: Secure access to SQL, NoSQL and Cloud databases with an all-in-one solution.
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|Google Cloud Datastore
|Microsoft SQL Server
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