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DBMS > Adabas vs. Google Cloud Datastore vs. HarperDB vs. HEAVY.AI vs. Microsoft Azure Data Explorer

System Properties Comparison Adabas vs. Google Cloud Datastore vs. HarperDB vs. HEAVY.AI vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameAdabas infodenotes "adaptable data base"  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHarperDB  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionOLTP - DBMS for mainframes and Linux/Unix/Windows environments infoused typically together with the Natural programming platformAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformHarperDB takes the "stack" out of "tech stack" by combining an ultra-fast document-style data store, in-memory cache, real-time message broker, and your application components into a single distributed technology.A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully managed big data interactive analytics platform
Primary database modelMultivalue DBMSDocument storeDocument storeRelational DBMSRelational DBMS infocolumn oriented
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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.64
Rank#104  Overall
#1  Multivalue DBMS
Score4.13
Rank#71  Overall
#12  Document stores
Score0.55
Rank#243  Overall
#38  Document stores
Score1.41
Rank#153  Overall
#71  Relational DBMS
Score3.28
Rank#83  Overall
#45  Relational DBMS
Websitewww.softwareag.com/­en_corporate/­platform/­adabas-natural.htmlcloud.google.com/­datastorewww.harperdb.iogithub.com/­heavyai/­heavydb
www.heavy.ai
azure.microsoft.com/­services/­data-explorer
Technical documentationcloud.google.com/­datastore/­docsdocs.harperdb.io/­docsdocs.heavy.aidocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperSoftware AGGoogleHarperDBHEAVY.AI, Inc.Microsoft
Initial release19712008201720162019
Current release3.1, August 20215.10, January 2022cloud service with continuous releases
License infoCommercial or Open Sourcecommercialcommercialcommercial infofree community edition availableOpen Source infoApache Version 2; enterprise edition availablecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnonoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageNode.jsC++ and CUDA
Server operating systemsBS2000
Linux
Unix
Windows
z/OS
z/VSE
hostedLinux
OS X
Linuxhosted
Data schemeyesschema-freedynamic schemayesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyes, details hereyes infoJSON data typesyesyes 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.nonononoyes
Secondary indexesyesyesyesnoall fields are automatically indexed
SQL infoSupport of SQLyes infowith add-on product Adabas SQL GatewaySQL-like query language (GQL)SQL-like data manipulation statementsyesKusto Query Language (KQL), SQL subset
APIs and other access methodsHTTP API infowith add-on software Adabas SOA Gateway
SOAP-based API infowith add-on software Adabas SOA Gateway
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket
JDBC
ODBC
Thrift
Vega
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesNatural.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift
All languages supporting JDBC/ODBC/Thrift
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresin Naturalusing Google App EngineCustom Functions infosince release 3.1noYes, possible languages: KQL, Python, R
TriggersnoCallbacks using the Google Apps Enginenonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesyes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas VistaShardingA table resides as a whole on one (or more) nodes in a clusterSharding infoRound robinSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyes, with add-on product Event ReplicatorMulti-source replication using Paxosyes infothe nodes on which a table resides can be definedMulti-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud DataflownonoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsAtomic execution of specific operationsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes, using LMDByesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyesno
User concepts infoAccess controlonly with OS-specific tools (e.g. IBM RACF, CA Top Secret)Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and rolesfine grained access rights according to SQL-standardAzure Active Directory Authentication

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More resources
Adabas infodenotes "adaptable data base"Google Cloud DatastoreHarperDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Data Explorer
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