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DBMS > Apache Druid vs. Google Cloud Datastore vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. SwayDB

System Properties Comparison Apache Druid vs. Google Cloud Datastore vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. SwayDB

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonSwayDB  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformFully managed big data interactive analytics platformWidely used in-process 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
Document storeRelational DBMS infocolumn orientedKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Key-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
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
Score4.36
Rank#72  Overall
#12  Document stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Score0.04
Rank#387  Overall
#61  Key-value stores
Websitedruid.apache.orgcloud.google.com/­datastoreazure.microsoft.com/­services/­data-explorerwww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlswaydb.simer.au
Technical documentationdruid.apache.org/­docs/­latest/­designcloud.google.com/­datastore/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oracle.com/­cd/­E17076_05/­html/­index.html
DeveloperApache Software Foundation and contributorsGoogleMicrosoftOracle infooriginally developed by Sleepycat, which was acquired by OracleSimer Plaha
Initial release20122008201919942018
Current release29.0.1, April 2024cloud service with continuous releases18.1.40, May 2020
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercialOpen Source infocommercial license 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, Java, C++ (depending on the Berkeley DB edition)Scala
Server operating systemsLinux
OS X
Unix
hostedhostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
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 dateyesyes, details hereyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesnono
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.nonoyesyes infoonly with the Berkeley DB XML editionno
Secondary indexesyesyesall fields are automatically indexedyesno
SQL infoSupport of SQLSQL for queryingSQL-like query language (GQL)Kusto Query Language (KQL), SQL subsetyes infoSQL interfaced based on SQLite is availableno
APIs and other access methodsJDBC
RESTful HTTP/JSON API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
Java
Kotlin
Scala
Server-side scripts infoStored proceduresnousing Google App EngineYes, possible languages: KQL, Python, Rnono
TriggersnoCallbacks using the Google Apps Engineyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoonly for the SQL APIno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingSharding infoImplicit feature of the cloud servicenonenone
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesMulti-source replication using Paxosyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud DataflowSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
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.Eventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACIDAtomic execution of operations
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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 Authenticationnono

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More resources
Apache DruidGoogle Cloud DatastoreMicrosoft Azure Data ExplorerOracle Berkeley DBSwayDB
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