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DBMS > EsgynDB vs. Google Cloud Bigtable vs. Kdb vs. Spark SQL

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. Kdb vs. Spark SQL

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Editorial information provided by DB-Engines
NameEsgynDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonKdb  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.High performance Time Series DBMSSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSKey-value store
Wide column store
Time Series DBMS
Vector DBMS
Relational DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.26
Rank#311  Overall
#141  Relational DBMS
Score3.86
Rank#90  Overall
#13  Key-value stores
#7  Wide column stores
Score7.69
Rank#53  Overall
#3  Time Series DBMS
#1  Vector DBMS
Score19.56
Rank#34  Overall
#21  Relational DBMS
Websitewww.esgyn.cncloud.google.com/­bigtablekx.comspark.apache.org/­sql
Technical documentationcloud.google.com/­bigtable/­docscode.kx.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperEsgynGoogleKx Systems, a division of First Derivatives plcApache Software Foundation
Initial release201520152000 infokdb was released 2000, kdb+ in 20032014
Current release3.6, May 20183.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercialcommercialcommercial infofree 32-bit versionOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++, JavaqScala
Server operating systemsLinuxhostedLinux
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesnoyesyes
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.nonoyesno
Secondary indexesyesnoyes infotable attribute 'grouped'no
SQL infoSupport of SQLyesnoSQL-like query language (q)SQL-like DML and DDL statements
APIs and other access methodsADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
Java
Python
R
Scala
Server-side scripts infoStored proceduresJava Stored Proceduresnouser defined functionsno
Triggersnonoyes infowith viewsno
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioningyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zonesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesno infosimilar paradigm used for internal processing
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency
Foreign keys infoReferential integrityyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsnono
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.nonoyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)rights management via user accountsno
More information provided by the system vendor
EsgynDBGoogle Cloud BigtableKdbSpark SQL
Specific characteristicsIntegrated columnar database & programming system for streaming, real time and historical...
» more
Competitive advantagesprovides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more
Typical application scenariostick database streaming sensor data massive intelligence applications oil and gas...
» more
Key customersGoldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more
Market metricskdb+ performance and reliability proven by our customers in critical infrastructure...
» more
Licensing and pricing modelsupon request
» more

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More resources
EsgynDBGoogle Cloud BigtableKdbSpark SQL
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