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DBMS > Databricks vs. Microsoft Azure SQL Database vs. Splice Machine

System Properties Comparison Databricks vs. Microsoft Azure SQL Database vs. Splice Machine

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Editorial information provided by DB-Engines
NameDatabricks  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Database as a Service offering with high compatibility to Microsoft SQL ServerOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument 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
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score77.99
Rank#16  Overall
#11  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitewww.databricks.comazure.microsoft.com/­en-us/­products/­azure-sql/­databasesplicemachine.com
Technical documentationdocs.databricks.comdocs.microsoft.com/­en-us/­azure/­azure-sqlsplicemachine.com/­how-it-works
DeveloperDatabricksMicrosoftSplice Machine
Initial release201320102014
Current releaseV123.1, March 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++Java
Server operating systemshostedhostedLinux
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or dateyesyes
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.yesyes
Secondary indexesyesyesyes
SQL infoSupport of SQLwith Databricks SQLyesyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languagesPython
R
Scala
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesTransact SQLyes infoJava
Triggersyesyes
Partitioning methods infoMethods for storing different data on different nodesShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes, with always 3 replicas availableMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
DatabricksMicrosoft Azure SQL Database infoformerly SQL AzureSplice Machine
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
DatabricksMicrosoft Azure SQL Database infoformerly SQL AzureSplice Machine
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