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DBMS > Atos Standard Common Repository vs. LeanXcale vs. Memcached vs. Microsoft Azure Data Explorer vs. Vertica

System Properties Comparison Atos Standard Common Repository vs. LeanXcale vs. Memcached vs. Microsoft Azure Data Explorer vs. Vertica

Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonLeanXcale  Xexclude from comparisonMemcached  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonVertica infoOpenText™ Vertica™  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 networksA highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesIn-memory key-value store, originally intended for cachingFully managed big data interactive analytics platformCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelDocument store
Key-value store
Key-value store
Relational DBMS
Key-value storeRelational DBMS infocolumn orientedRelational 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
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.29
Rank#291  Overall
#41  Key-value stores
#132  Relational DBMS
Score19.42
Rank#32  Overall
#4  Key-value stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorywww.leanxcale.comwww.memcached.orgazure.microsoft.com/­services/­data-explorerwww.vertica.com
Technical documentationgithub.com/­memcached/­memcached/­wikidocs.microsoft.com/­en-us/­azure/­data-explorervertica.com/­documentation
DeveloperAtos Convergence CreatorsLeanXcaleDanga Interactive infooriginally developed by Brad Fitzpatrick for LiveJournalMicrosoftOpenText infopreviously Micro Focus and Hewlett Packard
Initial release20162015200320192005
Current release17031.6.25, March 2024cloud service with continuous releases12.0.3, January 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoBSD licensecommercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononoyesno infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaCC++
Server operating systemsLinuxFreeBSD
Linux
OS X
Unix
Windows
hostedLinux
Data schemeSchema and schema-less with LDAP viewsyesschema-freeFixed schema with schema-less datatypes (dynamic)Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateoptionalnoyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.yesyesno
Secondary indexesyesnoall fields are automatically indexedNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoyes infothrough Apache DerbynoKusto Query Language (KQL), SQL subsetFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsLDAPJDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
Proprietary protocolMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesAll languages with LDAP bindingsC
Java
Scala
.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnonoYes, possible languages: KQL, Python, Ryes, PostgreSQL PL/pgSQL, with minor differences
Triggersyesnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionnoneSharding infoImplicit feature of the cloud servicehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesnone infoRepcached, a Memcached patch, provides this functionallityyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Multi-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsACIDnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesnoyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnono
User concepts infoAccess controlLDAP bind authenticationyes infousing SASL (Simple Authentication and Security Layer) protocolAzure Active Directory Authenticationfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Atos Standard Common RepositoryLeanXcaleMemcachedMicrosoft Azure Data ExplorerVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
» more

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Atos Standard Common RepositoryLeanXcaleMemcachedMicrosoft Azure Data ExplorerVertica infoOpenText™ Vertica™
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