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DBMS > CrateDB vs. Kyligence Enterprise vs. Sadas Engine vs. Teradata Aster

System Properties Comparison CrateDB vs. Kyligence Enterprise vs. Sadas Engine vs. Teradata Aster

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonKyligence Enterprise  Xexclude from comparisonSadas Engine  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionDistributed Database based on LuceneA distributed analytics engine for big data, built on top of Apache KylinSADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environmentsPlatform for big data analytics on multistructured data sources and types
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSRelational DBMSRelational DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score0.46
Rank#266  Overall
#124  Relational DBMS
Score0.07
Rank#373  Overall
#157  Relational DBMS
Websitecratedb.comkyligence.io/­kyligence-enterprisewww.sadasengine.com
Technical documentationcratedb.com/­docswww.sadasengine.com/­en/­sadas-engine-download-free-trial-and-documentation/­#documentation
DeveloperCrateKyligence, Inc.SADAS s.r.l.Teradata
Initial release2013201620062005
Current release8.0
License infoCommercial or Open SourceOpen Sourcecommercialcommercial infofree trial version availablecommercial
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 languageJavaJavaC++
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinuxAIX
Linux
Windows
Linux
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
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.nononoyes infoin Aster File Store
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilityANSI SQL for queries (using Apache Calcite)yesyes
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
ADO.NET
JDBC
ODBC
OLE DB
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
.Net
C
C#
C++
Groovy
Java
PHP
Python
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresuser defined functions (Javascript)noR packages
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelnoneyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnoyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyesno
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 strategyACID
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.nonoyes infomanaged by 'Learn by Usage'no
User concepts infoAccess controlrights management via user accountsAccess rights for users, groups and roles according to SQL-standardfine grained access rights according to SQL-standard
More information provided by the system vendor
CrateDBKyligence EnterpriseSadas EngineTeradata Aster
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
CrateDBKyligence EnterpriseSadas EngineTeradata Aster
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