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DBMS > Apache Pinot vs. Axibase vs. BigObject vs. CrateDB

System Properties Comparison Apache Pinot vs. Axibase vs. BigObject vs. CrateDB

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Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonAxibase  Xexclude from comparisonBigObject  Xexclude from comparisonCrateDB  Xexclude from comparison
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyScalable TimeSeries DBMS based on HBase with integrated rule engine and visualizationAnalytic DBMS for real-time computations and queriesDistributed Database based on Lucene
Primary database modelRelational DBMSTime Series DBMSRelational DBMS infoa hierachical model (tree) can be imposedDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.73
Rank#231  Overall
#107  Relational DBMS
Score0.33
Rank#292  Overall
#25  Time Series DBMS
Score0.17
Rank#333  Overall
#146  Relational DBMS
Score0.76
Rank#226  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#18  Time Series DBMS
#8  Vector DBMS
Websitepinot.apache.orgaxibase.com/­docs/­atsd/­financebigobject.iocratedb.com
Technical documentationdocs.pinot.apache.orgdocs.bigobject.iocratedb.com/­docs
DeveloperApache Software Foundation and contributorsAxibase CorporationBigObject, Inc.Crate
Initial release2015201320152013
Current release1.0.0, September 202315585
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infoCommunity Edition (single node) is free, Enterprise Edition (distributed) is paidcommercial infofree community edition availableOpen Source
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 systemsAll OS with a Java JDK11 or higherLinuxLinux 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 support
Data schemeyesyesyesFlexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateyesyes infoshort, integer, long, float, double, decimal, stringyesyes
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.nonono
Secondary indexesnoyesyes
SQL infoSupport of SQLSQL-like query languageSQL-like query languageSQL-like DML and DDL statementsyes, but no triggers and constraints, and PostgreSQL compatibility
APIs and other access methodsJDBCJDBC
Proprietary protocol (Network API)
RESTful HTTP API
fluentd
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
Supported programming languagesGo
Java
Python
Go
Java
PHP
Python
R
Ruby
.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
Server-side scripts infoStored proceduresyesLuauser defined functions (Javascript)
Triggersyesnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnoneConfigurable replication on table/partition-level
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneEventual Consistency
Read-after-write consistency on record level
Foreign keys infoReferential integritynoyes infoautomatically between fact table and dimension tablesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono infounique row identifiers can be used for implementing an optimistic concurrency control strategy
Concurrency infoSupport for concurrent manipulation of datayesyes infoRead/write lock on objects (tables, trees)yes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlnorights management via user accounts
More information provided by the system vendor
Apache PinotAxibaseBigObjectCrateDB
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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More resources
Apache PinotAxibaseBigObjectCrateDB
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