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DBMS > Atos Standard Common Repository vs. Databricks vs. Microsoft Azure Data Explorer

System Properties Comparison Atos Standard Common Repository vs. Databricks vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonDatabricks  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksThe 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.Fully managed big data interactive analytics platform
Primary database modelDocument store
Key-value store
Document store
Relational DBMS
Relational DBMS infocolumn oriented
Secondary database modelsDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.databricks.comazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.databricks.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperAtos Convergence CreatorsDatabricksMicrosoft
Initial release201620132019
Current release1703cloud service with continuous releases
License infoCommercial or Open Sourcecommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava
Server operating systemsLinuxhostedhosted
Data schemeSchema and schema-less with LDAP viewsFlexible Schema (defined schema, partial schema, schema free)Fixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateoptionalyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types
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.yesyesyes
Secondary indexesyesyesall fields are automatically indexed
SQL infoSupport of SQLnowith Databricks SQLKusto Query Language (KQL), SQL subset
APIs and other access methodsLDAPJDBC
ODBC
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesAll languages with LDAP bindingsPython
R
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresnouser defined functions and aggregatesYes, possible languages: KQL, Python, R
Triggersyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyesyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.yesnono
User concepts infoAccess controlLDAP bind authenticationAzure Active Directory Authentication
More information provided by the system vendor
Atos Standard Common RepositoryDatabricksMicrosoft Azure Data Explorer
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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Atos Standard Common RepositoryDatabricksMicrosoft Azure Data Explorer
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