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DBMS > Badger vs. Google BigQuery vs. Kyligence Enterprise vs. Warp 10

System Properties Comparison Badger vs. Google BigQuery vs. Kyligence Enterprise vs. Warp 10

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonGoogle BigQuery  Xexclude from comparisonKyligence Enterprise  Xexclude from comparisonWarp 10  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Large scale data warehouse service with append-only tablesA distributed analytics engine for big data, built on top of Apache KylinTimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase
Primary database modelKey-value storeRelational DBMSRelational DBMSTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.14
Rank#331  Overall
#49  Key-value stores
Score60.38
Rank#19  Overall
#13  Relational DBMS
Score0.40
Rank#269  Overall
#124  Relational DBMS
Score0.07
Rank#349  Overall
#32  Time Series DBMS
Websitegithub.com/­dgraph-io/­badgercloud.google.com/­bigquerykyligence.io/­kyligence-enterprisewww.warp10.io
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgercloud.google.com/­bigquery/­docswww.warp10.io/­content/­02_Getting_started
DeveloperDGraph LabsGoogleKyligence, Inc.SenX
Initial release2017201020162015
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialOpen Source infoApache License 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageGoJavaJava
Server operating systemsBSD
Linux
OS X
Solaris
Windows
hostedLinuxLinux
OS X
Windows
Data schemeschema-freeyesyesschema-free
Typing infopredefined data types such as float or datenoyesyesyes
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 indexesnonoyesno
SQL infoSupport of SQLnoyesANSI SQL for queries (using Apache Calcite)no
APIs and other access methodsRESTful HTTP/JSON APIJDBC
ODBC
RESTful HTTP API
HTTP API
Jupyter
WebSocket
Supported programming languagesGo.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby
Server-side scripts infoStored proceduresnouser defined functions infoin JavaScriptyes infoWarpScript
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesnonenoneSharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesnoneselectable replication factor infobased on HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyImmediate ConsistencyImmediate Consistency infobased on HBase
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infoSince BigQuery is designed for querying datano
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.nononoyes
User concepts infoAccess controlnoAccess privileges (owner, writer, reader) on dataset, table or view level infoGoogle Cloud Identity & Access Management (IAM)Mandatory use of cryptographic tokens, containing fine-grained authorizations

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More resources
BadgerGoogle BigQueryKyligence EnterpriseWarp 10
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