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DBMS > CrateDB vs. Ehcache vs. HEAVY.AI vs. Microsoft Azure Data Explorer vs. Tibero

System Properties Comparison CrateDB vs. Ehcache vs. HEAVY.AI vs. Microsoft Azure Data Explorer vs. Tibero

Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonEhcache  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonTibero  Xexclude from comparison
DescriptionDistributed Database based on LuceneA widely adopted Java cache with tiered storage optionsA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully managed big data interactive analytics platformA secure RDBMS, designed for easy portability from Oracle
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Key-value storeRelational DBMSRelational DBMS infocolumn orientedRelational DBMS
Secondary database modelsRelational DBMSSpatial 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
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score4.89
Rank#67  Overall
#8  Key-value stores
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score1.78
Rank#140  Overall
#64  Relational DBMS
Websitecratedb.comwww.ehcache.orggithub.com/­heavyai/­heavydb
www.heavy.ai
azure.microsoft.com/­services/­data-explorerus.tmaxsoft.com/­products/­tibero
Technical documentationcratedb.com/­docswww.ehcache.org/­documentationdocs.heavy.aidocs.microsoft.com/­en-us/­azure/­data-explorertechnet.tmaxsoft.com/­upload/­download/­online/­tibero/­pver-20150504-000002/­index.html
DeveloperCrateTerracotta Inc, owned by Software AGHEAVY.AI, Inc.MicrosoftTmaxSoft
Initial release20132009201620192003
Current release3.10.0, March 20225.10, January 2022cloud service with continuous releases6, April 2015
License infoCommercial or Open SourceOpen SourceOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache Version 2; enterprise edition availablecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenononoyesno
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 languageJavaJavaC++ and CUDAC and Assembler
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportAll OS with a Java VMLinuxhostedAIX
HP-UX
Linux
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyesFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.nononoyesyes
Secondary indexesyesnonoall fields are automatically indexedyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitynoyesKusto Query Language (KQL), SQL subsetyes
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JCacheJDBC
ODBC
Thrift
Vega
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI
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
JavaAll languages supporting JDBC/ODBC/Thrift
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic
Server-side scripts infoStored proceduresuser defined functions (Javascript)nonoYes, possible languages: KQL, Python, RPersistent Stored Procedure (PSM)
Triggersnoyes infoCache Event Listenersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoby using Terracotta ServerSharding infoRound robinSharding infoImplicit feature of the cloud servicehorizontal partitioning infoby range, hash, list or composite
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelyes infoby using Terracotta ServerMulti-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Tunable Consistency (Strong, Eventual, Weak)Immediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonononoyes
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 strategyyes infosupports JTA and can work as an XA resourcenonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyes infousing a tiered cache-storage approachyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesnono infoplanned for next version
User concepts infoAccess controlrights management via user accountsnofine grained access rights according to SQL-standardAzure Active Directory Authenticationfine grained access rights according to SQL-standard (SQL 92, SQL 99)
More information provided by the system vendor
CrateDBEhcacheHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Data ExplorerTibero
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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CrateDBEhcacheHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Data ExplorerTibero
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