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DBMS > CrateDB vs. DolphinDB vs. HBase vs. Microsoft Azure Data Explorer

System Properties Comparison CrateDB vs. DolphinDB vs. HBase vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonDolphinDB  Xexclude from comparisonHBase  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionDistributed Database based on LuceneDolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.Wide-column store based on Apache Hadoop and on concepts of BigTableFully managed big data interactive analytics platform
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Time Series DBMSWide column storeRelational DBMS infocolumn oriented
Secondary database modelsRelational DBMSRelational DBMSDocument 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
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score4.03
Rank#78  Overall
#6  Time Series DBMS
Score27.97
Rank#26  Overall
#2  Wide column stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Websitecratedb.comwww.dolphindb.comhbase.apache.orgazure.microsoft.com/­services/­data-explorer
Technical documentationcratedb.com/­docsdocs.dolphindb.cn/­en/­help200/­index.htmlhbase.apache.org/­book.htmldocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperCrateDolphinDB, IncApache Software Foundation infoApache top-level project, originally developed by PowersetMicrosoft
Initial release2013201820082019
Current releasev2.00.4, January 20222.3.4, January 2021cloud service with continuous releases
License infoCommercial or Open SourceOpen Sourcecommercial infofree community version availableOpen Source infoApache version 2commercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageJavaC++Java
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinux
Windows
Linux
Unix
Windows infousing Cygwin
hosted
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-free, schema definition possibleFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesoptions to bring your own types, AVROyes 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.nononoyes
Secondary indexesyesyesnoall fields are automatically indexed
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitySQL-like query languagenoKusto Query Language (KQL), SQL subset
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket
Java API
RESTful HTTP API
Thrift
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languages.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust
C
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresuser defined functions (Javascript)yesyes infoCoprocessors in JavaYes, possible languages: KQL, Python, R
Triggersnonoyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioningShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelyesMulti-source replication
Source-replica replication
yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyesSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate Consistency or Eventual ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infounique row identifiers can be used for implementing an optimistic concurrency control strategyyesSingle row ACID (across millions of columns)no
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesno
User concepts infoAccess controlrights management via user accountsAdministrators, Users, GroupsAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACAzure Active Directory Authentication
More information provided by the system vendor
CrateDBDolphinDBHBaseMicrosoft Azure Data Explorer
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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CrateDBDolphinDBHBaseMicrosoft Azure Data Explorer
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