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DBMS > Google Cloud Bigtable vs. Manticore Search vs. Microsoft Azure Data Explorer vs. NSDb vs. Riak KV

System Properties Comparison Google Cloud Bigtable vs. Manticore Search vs. Microsoft Azure Data Explorer vs. NSDb vs. Riak KV

Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonManticore Search  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonNSDb  Xexclude from comparisonRiak KV  Xexclude from comparison
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Multi-storage database for search, including full-text search.Fully managed big data interactive analytics platformScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesDistributed, fault tolerant key-value store
Primary database modelKey-value store
Wide column store
Search engineRelational DBMS infocolumn orientedTime Series DBMSKey-value store infowith links between data sets and object tags for the creation of secondary indexes
Secondary database modelsTime Series DBMS infousing the Manticore Columnar LibraryDocument 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
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score0.29
Rank#302  Overall
#21  Search engines
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Score4.01
Rank#79  Overall
#9  Key-value stores
Websitecloud.google.com/­bigtablemanticoresearch.comazure.microsoft.com/­services/­data-explorernsdb.io
Technical documentationcloud.google.com/­bigtable/­docsmanual.manticoresearch.comdocs.microsoft.com/­en-us/­azure/­data-explorernsdb.io/­Architecturewww.tiot.jp/­riak-docs/­riak/­kv/­latest
DeveloperGoogleManticore SoftwareMicrosoftOpenSource, formerly Basho Technologies
Initial release20152017201920172009
Current release6.0, February 2023cloud service with continuous releases3.2.0, December 2022
License infoCommercial or Open SourcecommercialOpen Source infoGPL version 2commercialOpen Source infoApache Version 2.0Open Source infoApache version 2, commercial enterprise edition
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++Java, ScalaErlang
Server operating systemshostedFreeBSD
Linux
macOS
Windows
hostedLinux
macOS
Linux
OS X
Data schemeschema-freeFixed schemaFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or datenoInt, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Booleanyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes: int, bigint, decimal, stringno
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.noCan index from XMLyesnono
Secondary indexesnoyes infofull-text index on all search fieldsall fields are automatically indexedall fields are automatically indexedrestricted
SQL infoSupport of SQLnoSQL-like query languageKusto Query Language (KQL), SQL subsetSQL-like query languageno
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
gRPC
HTTP REST
WebSocket
HTTP API
Native Erlang Interface
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Java
Scala
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresnouser defined functionsYes, possible languages: KQL, Python, RnoErlang
Triggersnonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding infoImplicit feature of the cloud serviceShardingSharding infono "single point of failure"
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesSynchronous replication based on Galera libraryyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual Consistency
Immediate Consistency
Eventual ConsistencyEventual Consistency
Foreign keys infoReferential integritynonononono infolinks between data sets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsyes infoisolated transactions for atomic changes and binary logging for safe writesnonono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyes infoThe original contents of fields are not stored in the Manticore index.yesUsing Apache Luceneyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noAzure Active Directory Authenticationyes, using Riak Security

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More resources
Google Cloud BigtableManticore SearchMicrosoft Azure Data ExplorerNSDbRiak KV
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