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DBMS > Apache Druid vs. Datastax Enterprise vs. Microsoft Azure Data Explorer vs. OpenMLDB vs. SwayDB

System Properties Comparison Apache Druid vs. Datastax Enterprise vs. Microsoft Azure Data Explorer vs. OpenMLDB vs. SwayDB

Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonDatastax Enterprise  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOpenMLDB  Xexclude from comparisonSwayDB  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataDataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.Fully managed big data interactive analytics platformAn open-source machine learning database that provides a feature platform for training and inferenceAn embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage
Primary database modelRelational DBMS
Time Series DBMS
Wide column storeRelational DBMS infocolumn orientedTime Series DBMSKey-value store
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS
Document 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
Relational DBMS
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
Score5.93
Rank#56  Overall
#4  Wide column stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.10
Rank#359  Overall
#36  Time Series DBMS
Score0.04
Rank#387  Overall
#61  Key-value stores
Websitedruid.apache.orgwww.datastax.com/­products/­datastax-enterpriseazure.microsoft.com/­services/­data-exploreropenmldb.aiswaydb.simer.au
Technical documentationdruid.apache.org/­docs/­latest/­designdocs.datastax.comdocs.microsoft.com/­en-us/­azure/­data-exploreropenmldb.ai/­docs/­zh/­main
DeveloperApache Software Foundation and contributorsDataStaxMicrosoft4 Paradigm Inc.Simer Plaha
Initial release20122011201920202018
Current release29.0.1, April 20246.8, April 2020cloud service with continuous releases2024-2 February 2024
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercialOpen SourceOpen Source infoGNU Affero GPL V3.0
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.
Implementation languageJavaJavaC++, Java, ScalaScala
Server operating systemsLinux
OS X
Unix
Linux
OS X
hostedLinux
Data schemeyes infoschema-less columns are supportedschema-freeFixed schema with schema-less datatypes (dynamic)Fixed schemaschema-free
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesno
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 indexesyesyesall fields are automatically indexedyesno
SQL infoSupport of SQLSQL for queryingSQL-like DML and DDL statements (CQL); Spark SQLKusto Query Language (KQL), SQL subsetyesno
APIs and other access methodsJDBC
RESTful HTTP/JSON API
Proprietary protocol infoCQL (Cassandra Query Language)
TinkerPop Gremlin infowith DSE Graph
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
SQLAlchemy
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C++
Go
Java
Python
Scala
Java
Kotlin
Scala
Server-side scripts infoStored proceduresnonoYes, possible languages: KQL, Python, Rnono
Triggersnoyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynono
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedSharding infono "single point of failure"Sharding infoImplicit feature of the cloud servicehorizontal partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesconfigurable replication factor, datacenter aware, advanced replication for edge computingyes 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 methodsnoyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Tunable Consistency infoconsistency level can be individually decided with each write operation
Eventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infoAtomicity and isolation are supported for single operationsnonoAtomic 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.noyesnoyesyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users can be defined per objectAzure Active Directory Authenticationfine grained access rights according to SQL-standardno
More information provided by the system vendor
Apache DruidDatastax EnterpriseMicrosoft Azure Data ExplorerOpenMLDBSwayDB
Specific characteristicsDataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more
Competitive advantagesSupporting the following application requirements: Zero downtime - Built on Apache...
» more
Typical application scenariosApplications that must be massively and linearly scalable with 100% uptime and able...
» more
Key customersCapital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
» more
Market metricsAmong the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
» more
Licensing and pricing modelsAnnual subscription
» more

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More resources
Apache DruidDatastax EnterpriseMicrosoft Azure Data ExplorerOpenMLDBSwayDB
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