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DBMS > Apache Pinot vs. Microsoft Azure Data Explorer vs. NSDb vs. Sadas Engine vs. Vertica

System Properties Comparison Apache Pinot vs. Microsoft Azure Data Explorer vs. NSDb vs. Sadas Engine vs. Vertica

Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonNSDb  Xexclude from comparisonSadas Engine  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyFully managed big data interactive analytics platformScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesSADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environmentsCloud 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 modelRelational DBMSRelational DBMS infocolumn orientedTime Series DBMSRelational DBMSRelational 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
Score0.40
Rank#270  Overall
#125  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.00
Rank#383  Overall
#41  Time Series DBMS
Score0.00
Rank#383  Overall
#158  Relational DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitepinot.apache.orgazure.microsoft.com/­services/­data-explorernsdb.iowww.sadasengine.comwww.vertica.com
Technical documentationdocs.pinot.apache.orgdocs.microsoft.com/­en-us/­azure/­data-explorernsdb.io/­Architecturewww.sadasengine.com/­en/­sadas-engine-download-free-trial-and-documentation/­#documentationvertica.com/­documentation
DeveloperApache Software Foundation and contributorsMicrosoftSADAS s.r.l.OpenText infopreviously Micro Focus and Hewlett Packard
Initial release20152019201720062005
Current release1.0.0, September 2023cloud service with continuous releases8.012.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2.0commercial infofree trial version availablecommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenoyesnonono 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 languageJavaJava, ScalaC++C++
Server operating systemsAll OS with a Java JDK11 or higherhostedLinux
macOS
AIX
Linux
Windows
Linux
Data schemeyesFixed schema with schema-less datatypes (dynamic)yesYes, 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 dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes: int, bigint, decimal, stringyesyes
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.yesnonono
Secondary indexesall fields are automatically indexedall fields are automatically indexedyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL-like query languageKusto Query Language (KQL), SQL subsetSQL-like query languageyesFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBCMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
gRPC
HTTP REST
WebSocket
JDBC
ODBC
Proprietary protocol
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesGo
Java
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Java
Scala
.Net
C
C#
C++
Groovy
Java
PHP
Python
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresYes, possible languages: KQL, Python, Rnonoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningSharding infoImplicit feature of the cloud serviceShardinghorizontal partitioninghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.noneMulti-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 methodsSpark connector (open source): github.com/­Azure/­azure-kusto-sparknonono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Eventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesUsing Apache Luceneyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infomanaged by 'Learn by Usage'no
User concepts infoAccess controlAzure Active Directory AuthenticationAccess rights for users, groups and roles according to SQL-standardfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Apache PinotMicrosoft Azure Data ExplorerNSDbSadas EngineVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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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...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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More resources
Apache PinotMicrosoft Azure Data ExplorerNSDbSadas EngineVertica infoOpenText™ Vertica™
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