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DBMS > Apache Pinot vs. Axibase vs. CrateDB vs. Microsoft Azure Table Storage

System Properties Comparison Apache Pinot vs. Axibase vs. CrateDB vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonAxibase  Xexclude from comparisonCrateDB  Xexclude from comparisonMicrosoft Azure Table Storage  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 visualizationDistributed Database based on LuceneA Wide Column Store for rapid development using massive semi-structured datasets
Primary database modelRelational DBMSTime Series DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Wide column store
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.38
Rank#275  Overall
#126  Relational DBMS
Score0.35
Rank#282  Overall
#25  Time Series DBMS
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score4.04
Rank#77  Overall
#6  Wide column stores
Websitepinot.apache.orgaxibase.com/­docs/­atsd/­financecratedb.comazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationdocs.pinot.apache.orgcratedb.com/­docs
DeveloperApache Software Foundation and contributorsAxibase CorporationCrateMicrosoft
Initial release2015201320132012
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 paidOpen Sourcecommercial
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 languageJavaJavaJava
Server operating systemsAll OS with a Java JDK11 or higherLinuxAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthosted
Data schemeyesyesFlexible Schema (defined schema, partial schema, schema free)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 indexesnoyesno
SQL infoSupport of SQLSQL-like query languageSQL-like query languageyes, but no triggers and constraints, and PostgreSQL compatibilityno
APIs and other access methodsJDBCJDBC
Proprietary protocol (Network API)
RESTful HTTP API
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
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
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyesuser defined functions (Javascript)no
Triggersyesnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationConfigurable replication on table/partition-levelyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate Consistency
Foreign keys infoReferential integritynonono
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 strategyoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.nono
User concepts infoAccess controlrights management via user accountsAccess rights based on private key authentication or shared access signatures
More information provided by the system vendor
Apache PinotAxibaseCrateDBMicrosoft Azure Table Storage
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 PinotAxibaseCrateDBMicrosoft Azure Table Storage
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