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DBMS > Apache Druid vs. Google Cloud Bigtable vs. Microsoft Azure Data Explorer vs. NCache vs. SwayDB

System Properties Comparison Apache Druid vs. Google Cloud Bigtable vs. Microsoft Azure Data Explorer vs. NCache vs. SwayDB

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonNCache  Xexclude from comparisonSwayDB  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Fully managed big data interactive analytics platformOpen-Source and Enterprise in-memory Key-Value StoreAn embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage
Primary database modelRelational DBMS
Time Series DBMS
Key-value store
Wide column store
Relational DBMS infocolumn orientedKey-value storeKey-value store
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
Document store
Search engine infoUsing distributed Lucene
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.96
Rank#195  Overall
#29  Key-value stores
Score0.04
Rank#387  Overall
#61  Key-value stores
Websitedruid.apache.orgcloud.google.com/­bigtableazure.microsoft.com/­services/­data-explorerwww.alachisoft.com/­ncacheswaydb.simer.au
Technical documentationdruid.apache.org/­docs/­latest/­designcloud.google.com/­bigtable/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerwww.alachisoft.com/­resources/­docs
DeveloperApache Software Foundation and contributorsGoogleMicrosoftAlachisoftSimer Plaha
Initial release20122015201920052018
Current release29.0.1, April 2024cloud service with continuous releases5.3.3, April 2024
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercialOpen Source infoEnterprise Edition availableOpen Source infoGNU Affero GPL V3.0
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC#, .NET, .NET Core, JavaScala
Server operating systemsLinux
OS X
Unix
hostedhostedLinux
Windows
Data schemeyes infoschema-less columns are supportedschema-freeFixed schema with schema-less datatypes (dynamic)schema-freeschema-free
Typing infopredefined data types such as float or dateyesnoyes 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 Counterno
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.nonoyesnono
Secondary indexesyesnoall fields are automatically indexedyesno
SQL infoSupport of SQLSQL for queryingnoKusto Query Language (KQL), SQL subsetSQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.no
APIs and other access methodsJDBC
RESTful HTTP/JSON API
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
IDistributedCache
JCache
LINQ
Proprietary native API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala
Java
Kotlin
Scala
Server-side scripts infoStored proceduresnonoYes, possible languages: KQL, Python, Rno infosupport for stored procedures with SQL-Server CLRno
Triggersnonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoNotificationsno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingSharding infoImplicit feature of the cloud serviceyesnone
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesInternal replication in Colossus, and regional replication between two clusters in different zonesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes, with selectable consistency levelnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkyesno
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
Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology
Immediate Consistency
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic single-row operationsnooptimistic locking and pessimistic lockingAtomic execution of operations
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.nononoyesyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Azure Active Directory AuthenticationAuthentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)no
More information provided by the system vendor
Apache DruidGoogle Cloud BigtableMicrosoft Azure Data ExplorerNCacheSwayDB
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
Apache DruidGoogle Cloud BigtableMicrosoft Azure Data ExplorerNCacheSwayDB
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