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DBMS > Google BigQuery vs. Ignite vs. Microsoft SQL Server vs. Splice Machine

System Properties Comparison Google BigQuery vs. Ignite vs. Microsoft SQL Server vs. Splice Machine

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Editorial information provided by DB-Engines
NameGoogle BigQuery  Xexclude from comparisonIgnite  Xexclude from comparisonMicrosoft SQL Server  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionLarge scale data warehouse service with append-only tablesApache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.Microsofts flagship relational DBMSOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSKey-value store
Relational DBMS
Relational DBMSRelational DBMS
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score60.38
Rank#19  Overall
#13  Relational DBMS
Score3.16
Rank#96  Overall
#15  Key-value stores
#49  Relational DBMS
Score824.29
Rank#3  Overall
#3  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitecloud.google.com/­bigqueryignite.apache.orgwww.microsoft.com/­en-us/­sql-serversplicemachine.com
Technical documentationcloud.google.com/­bigquery/­docsapacheignite.readme.io/­docslearn.microsoft.com/­en-US/­sql/­sql-serversplicemachine.com/­how-it-works
DeveloperGoogleApache Software FoundationMicrosoftSplice Machine
Initial release2010201519892014
Current releaseApache Ignite 2.6SQL Server 2022, November 20223.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercial inforestricted free version is availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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SQLServer Flex @ STACKIT offers a managed version of SQL Server with adjustable CPU, RAM, storage amount and speed, in enterprise grade to perfectly match all application requirements. All services are 100% GDPR-compliant.
Implementation languageC++, Java, .NetC++Java
Server operating systemshostedLinux
OS X
Solaris
Windows
Linux
Windows
Linux
OS X
Solaris
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.noyesyes
Secondary indexesnoyesyesyes
SQL infoSupport of SQLyesANSI-99 for query and DML statements, subset of DDLyesyes
APIs and other access methodsRESTful HTTP/JSON APIHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)
JDBC
Native Spark Datasource
ODBC
Supported programming languages.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby
C#
C++
Java
PHP
Python
Ruby
Scala
C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions infoin JavaScriptyes (compute grid and cache interceptors can be used instead)Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Javayes infoJava
Triggersnoyes (cache interceptors and events)yesyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingtables can be distributed across several files (horizontal partitioning); sharding through federationShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)yes, but depending on the SQL-Server EditionMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)noYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infoSince BigQuery is designed for querying dataACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
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.noyesyesyes
User concepts infoAccess controlAccess privileges (owner, writer, reader) on dataset, table or view level infoGoogle Cloud Identity & Access Management (IAM)Security Hooks for custom implementationsfine grained access rights according to SQL-standardAccess rights for users, groups and roles according to SQL-standard

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More resources
Google BigQueryIgniteMicrosoft SQL ServerSplice Machine
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