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DBMS > HEAVY.AI vs. Microsoft Azure Data Explorer vs. NCache vs. Oracle Rdb vs. Sqrrl

System Properties Comparison HEAVY.AI vs. Microsoft Azure Data Explorer vs. NCache vs. Oracle Rdb vs. Sqrrl

Editorial information provided by DB-Engines
NameHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonNCache  Xexclude from comparisonOracle Rdb  Xexclude from comparisonSqrrl  Xexclude from comparison
Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.
DescriptionA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully managed big data interactive analytics platformOpen-Source and Enterprise in-memory Key-Value StoreAdaptable, secure NoSQL built on Apache Accumulo
Primary database modelRelational DBMSRelational DBMS infocolumn orientedKey-value storeRelational DBMSDocument store
Graph DBMS
Key-value store
Wide column store
Secondary database modelsSpatial DBMSDocument 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
Document store
Search engine infoUsing distributed Lucene
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.94
Rank#195  Overall
#29  Key-value stores
Score1.08
Rank#184  Overall
#85  Relational DBMS
Websitegithub.com/­heavyai/­heavydb
www.heavy.ai
azure.microsoft.com/­services/­data-explorerwww.alachisoft.com/­ncachewww.oracle.com/­database/­technologies/­related/­rdb.htmlsqrrl.com
Technical documentationdocs.heavy.aidocs.microsoft.com/­en-us/­azure/­data-explorerwww.alachisoft.com/­resources/­docswww.oracle.com/­database/­technologies/­related/­rdb-doc.html
DeveloperHEAVY.AI, Inc.MicrosoftAlachisoftOracle, originally developed by Digital Equipment Corporation (DEC)Amazon infooriginally Sqrrl Data, Inc.
Initial release20162019200519842012
Current release5.10, January 2022cloud service with continuous releases5.3.3, April 20247.4.1.1, 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2; enterprise edition availablecommercialOpen Source infoEnterprise Edition availablecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ and CUDAC#, .NET, .NET Core, JavaJava
Server operating systemsLinuxhostedLinux
Windows
HP Open VMSLinux
Data schemeyesFixed schema with schema-less datatypes (dynamic)schema-freeFlexible Schema (defined schema, partial schema, schema free)schema-free
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-typespartial infoSupported data types are Lists, Queues, Hashsets, Dictionary and Counteryesyes
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.noyesnono
Secondary indexesnoall fields are automatically indexedyesyesyes
SQL infoSupport of SQLyesKusto Query Language (KQL), SQL subsetSQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.yesno
APIs and other access methodsJDBC
ODBC
Thrift
Vega
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
IDistributedCache
JCache
LINQ
Proprietary native API
Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift
Supported programming languagesAll languages supporting JDBC/ODBC/Thrift
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala
Actionscript
C infousing GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk
Server-side scripts infoStored proceduresnoYes, possible languages: KQL, Python, Rno infosupport for stored procedures with SQL-Server CLRno
Triggersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoNotificationsno
Partitioning methods infoMethods for storing different data on different nodesSharding infoRound robinSharding infoImplicit feature of the cloud serviceyesSharding infomaking use of Hadoop
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes, with selectable consistency levelselectable replication factor infomaking use of Hadoop
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkyesnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology
Immediate ConsistencyImmediate Consistency infoDocument store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonooptimistic locking and pessimistic lockingyes, on a single nodeAtomic updates per row, document, or graph entity
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAzure Active Directory AuthenticationAuthentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)
More information provided by the system vendor
HEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Data ExplorerNCacheOracle RdbSqrrl
Specific characteristicsNCache has been the market leader in .NET Distributed Caching since 2005 . NCache...
» more
Competitive advantagesNCache is 100% .NET/ .NET Core based which fully supports ASP.NET Core Sessions ,...
» more
Typical application scenariosNCache enables industries like retail, finance, banking IoT, travel, ecommerce, healthcare...
» more
Key customersBank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
» more
Market metricsMarket Leader in .NET Distributed Caching since 2005.
» more
Licensing and pricing modelsNCache Open Source is free on an as-is basis without any support. NCache Enterprise...
» more

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More resources
HEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Data ExplorerNCacheOracle RdbSqrrl
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