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DBMS > EsgynDB vs. Google Cloud Bigtable vs. JSqlDb vs. Microsoft Azure Synapse Analytics vs. Oracle Berkeley DB

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. JSqlDb vs. Microsoft Azure Synapse Analytics vs. Oracle Berkeley DB

Editorial information provided by DB-Engines
NameEsgynDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonJSqlDb  Xexclude from comparisonMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data Warehouse  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparison
JSqlDB seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.
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.JavaScript Query Language Database, stores JavaScript objects and primitivesElastic, large scale data warehouse service leveraging the broad eco-system of SQL ServerWidely used in-process key-value store
Primary database modelRelational DBMSKey-value store
Wide column store
Document store
Object oriented DBMS
Relational DBMSKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score20.56
Rank#31  Overall
#19  Relational DBMS
Score2.21
Rank#117  Overall
#20  Key-value stores
#3  Native XML DBMS
Websitewww.esgyn.cncloud.google.com/­bigtablejsqldb.org (offline)azure.microsoft.com/­services/­synapse-analyticswww.oracle.com/­database/­technologies/­related/­berkeleydb.html
Technical documentationcloud.google.com/­bigtable/­docsdocs.microsoft.com/­azure/­synapse-analyticsdocs.oracle.com/­cd/­E17076_05/­html/­index.html
DeveloperEsgynGoogleKonrad von BackstromMicrosoftOracle infooriginally developed by Sleepycat, which was acquired by Oracle
Initial release20152015201820161994
Current release0.8, December 201818.1.40, May 2020
License infoCommercial or Open SourcecommercialcommercialOpen SourcecommercialOpen Source infocommercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++, JavaC++C, Java, C++ (depending on the Berkeley DB edition)
Server operating systemsLinuxhostedLinux
macOS
Windows
hostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Data schemeyesschema-freeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesnonoyesno
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.nonononoyes infoonly with the Berkeley DB XML edition
Secondary indexesyesnonoyesyes
SQL infoSupport of SQLyesnonoyesyes infoSQL interfaced based on SQLite is available
APIs and other access methodsADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
ADO.NET
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
JavaScriptC#
Java
PHP
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
Server-side scripts infoStored proceduresJava Stored Proceduresnofunctions in JavaScriptTransact SQLno
Triggersnonononoyes infoonly for the SQL API
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnoneSharding, horizontal partitioningnone
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 zonesnoneyesSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesnonono
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 integrityyesnonono infodocs.microsoft.com/­en-us/­azure/­synapse-analytics/­sql-data-warehouse/­sql-data-warehouse-table-constraintsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes infousing RocksDByesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
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)yesno

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More resources
EsgynDBGoogle Cloud BigtableJSqlDbMicrosoft Azure Synapse Analytics infopreviously named Azure SQL Data WarehouseOracle Berkeley DB
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