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DBMS > Drizzle vs. Fauna vs. Google Cloud Bigtable vs. Manticore Search vs. Microsoft Azure Data Explorer

System Properties Comparison Drizzle vs. Fauna vs. Google Cloud Bigtable vs. Manticore Search vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonFauna infopreviously named FaunaDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonManticore Search  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.Google'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 platform
Primary database modelRelational DBMSDocument store
Graph DBMS
Relational DBMS
Time Series DBMS
Key-value store
Wide column store
Search engineRelational DBMS infocolumn oriented
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
Score1.52
Rank#153  Overall
#26  Document stores
#14  Graph DBMS
#71  Relational DBMS
#13  Time Series DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score0.22
Rank#312  Overall
#21  Search engines
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitefauna.comcloud.google.com/­bigtablemanticoresearch.comazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.fauna.comcloud.google.com/­bigtable/­docsmanual.manticoresearch.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperDrizzle project, originally started by Brian AkerFauna, Inc.GoogleManticore SoftwareMicrosoft
Initial release20082014201520172019
Current release7.2.4, September 20126.0, February 2023cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialcommercialOpen Source infoGPL version 2commercial
Cloud-based only infoOnly available as a cloud servicenoyesyesnoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ScalaC++
Server operating systemsFreeBSD
Linux
OS X
hostedhostedFreeBSD
Linux
macOS
Windows
hosted
Data schemeyesschema-freeschema-freeFixed schemaFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesnonoInt, 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-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.nonoCan index from XMLyes
Secondary indexesyesyesnoyes infofull-text index on all search fieldsall fields are automatically indexed
SQL infoSupport of SQLyes infowith proprietary extensionsnonoSQL-like query languageKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBCRESTful HTTP APIgRPC (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
Supported programming languagesC
C++
Java
PHP
C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift
C#
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
Server-side scripts infoStored proceduresnouser defined functionsnouser defined functionsYes, possible languages: KQL, Python, R
Triggersno infohooks for callbacks inside the server can be used.nononoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning infoconsistent hashingShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replicationInternal 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.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesnoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDAtomic single-row operationsyes infoisolated transactions for atomic changes and binary logging for safe writesno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Manticore index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPIdentity management, authentication, and access controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noAzure Active Directory Authentication

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More resources
DrizzleFauna infopreviously named FaunaDBGoogle Cloud BigtableManticore SearchMicrosoft Azure Data Explorer
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