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DBMS > BigObject vs. CrateDB vs. H2 vs. Warp 10

System Properties Comparison BigObject vs. CrateDB vs. H2 vs. Warp 10

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Editorial information provided by DB-Engines
NameBigObject  Xexclude from comparisonCrateDB  Xexclude from comparisonH2  Xexclude from comparisonWarp 10  Xexclude from comparison
DescriptionAnalytic DBMS for real-time computations and queriesDistributed Database based on LuceneFull-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase
Primary database modelRelational DBMS infoa hierachical model (tree) can be imposedDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSTime Series DBMS
Secondary database modelsRelational DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#333  Overall
#147  Relational DBMS
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score8.13
Rank#49  Overall
#31  Relational DBMS
Score0.07
Rank#349  Overall
#32  Time Series DBMS
Websitebigobject.iocratedb.comwww.h2database.comwww.warp10.io
Technical documentationdocs.bigobject.iocratedb.com/­docswww.h2database.com/­html/­main.htmlwww.warp10.io/­content/­02_Getting_started
DeveloperBigObject, Inc.CrateThomas MuellerSenX
Initial release2015201320052015
Current release2.2.220, July 2023
License infoCommercial or Open Sourcecommercial infofree community edition availableOpen SourceOpen Source infodual-licence (Mozilla public license, Eclipse public license)Open Source infoApache License 2.0
Cloud-based only infoOnly available as a cloud servicenononono
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 languageJavaJavaJava
Server operating systemsLinux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportAll OS with a Java VMLinux
OS X
Windows
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yesschema-free
Typing infopredefined data types such as float or dateyesyesyesyes
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.nononono
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsyes, but no triggers and constraints, and PostgreSQL compatibilityyesno
APIs and other access methodsfluentd
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBC
ODBC
HTTP API
Jupyter
WebSocket
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
Java
Server-side scripts infoStored proceduresLuauser defined functions (Javascript)Java Stored Procedures and User-Defined Functionsyes infoWarpScript
Triggersnonoyesno
Partitioning methods infoMethods for storing different data on different nodesnoneShardingnoneSharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesnoneConfigurable replication on table/partition-levelWith clustering: 2 database servers on different computers operate on identical copies of a databaseselectable replication factor infobased on HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate Consistency infobased on HBase
Foreign keys infoReferential integrityyes infoautomatically between fact table and dimension tablesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infounique row identifiers can be used for implementing an optimistic concurrency control strategyACIDno
Concurrency infoSupport for concurrent manipulation of datayes infoRead/write lock on objects (tables, trees)yesyes, multi-version concurrency control (MVCC)yes
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.yesnoyesyes
User concepts infoAccess controlnorights management via user accountsfine grained access rights according to SQL-standardMandatory use of cryptographic tokens, containing fine-grained authorizations
More information provided by the system vendor
BigObjectCrateDBH2Warp 10
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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BigObjectCrateDBH2Warp 10
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