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DBMS > Adabas vs. Apache Phoenix vs. Badger vs. CrateDB

System Properties Comparison Adabas vs. Apache Phoenix vs. Badger vs. CrateDB

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Editorial information provided by DB-Engines
NameAdabas infodenotes "adaptable data base"  Xexclude from comparisonApache Phoenix  Xexclude from comparisonBadger  Xexclude from comparisonCrateDB  Xexclude from comparison
DescriptionOLTP - DBMS for mainframes and Linux/Unix/Windows environments infoused typically together with the Natural programming platformA scale-out RDBMS with evolutionary schema built on Apache HBaseAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Distributed Database based on Lucene
Primary database modelMultivalue DBMSRelational DBMSKey-value storeDocument 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
Score3.17
Rank#94  Overall
#1  Multivalue DBMS
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score0.14
Rank#331  Overall
#49  Key-value stores
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Websitewww.softwareag.com/­en_corporate/­platform/­adabas-natural.htmlphoenix.apache.orggithub.com/­dgraph-io/­badgercratedb.com
Technical documentationphoenix.apache.orggodoc.org/­github.com/­dgraph-io/­badgercratedb.com/­docs
DeveloperSoftware AGApache Software FoundationDGraph LabsCrate
Initial release1971201420172013
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 2019
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoApache 2.0Open 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 languageJavaGoJava
Server operating systemsBS2000
Linux
Unix
Windows
z/OS
z/VSE
Linux
Unix
Windows
BSD
Linux
OS X
Solaris
Windows
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support
Data schemeyesyes infolate-bound, schema-on-read capabilitiesschema-freeFlexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateyesyesnoyes
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 indexesyesyesnoyes
SQL infoSupport of SQLyes infowith add-on product Adabas SQL Gatewayyesnoyes, but no triggers and constraints, and PostgreSQL compatibility
APIs and other access methodsHTTP API infowith add-on software Adabas SOA Gateway
SOAP-based API infowith add-on software Adabas SOA Gateway
JDBCADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
Supported programming languagesNaturalC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
Go.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 proceduresin Naturaluser defined functionsnouser defined functions (Javascript)
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesyes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas VistaShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, with add-on product Event ReplicatorMulti-source replication
Source-replica replication
noneConfigurable replication on table/partition-level
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoHadoop integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual ConsistencynoneEventual Consistency
Read-after-write consistency on record level
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnono infounique row identifiers can be used for implementing an optimistic concurrency control strategy
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.noyesnono
User concepts infoAccess controlonly with OS-specific tools (e.g. IBM RACF, CA Top Secret)Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancynorights management via user accounts
More information provided by the system vendor
Adabas infodenotes "adaptable data base"Apache PhoenixBadgerCrateDB
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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Adabas infodenotes "adaptable data base"Apache PhoenixBadgerCrateDB
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