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DBMS > Dgraph vs. EsgynDB vs. Google Cloud Bigtable vs. Microsoft Access vs. Spark SQL

System Properties Comparison Dgraph vs. EsgynDB vs. Google Cloud Bigtable vs. Microsoft Access vs. Spark SQL

Editorial information provided by DB-Engines
NameDgraph  Xexclude from comparisonEsgynDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionDistributed and scalable native Graph DBMSEnterprise-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.Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries. infoThe Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelGraph DBMSRelational DBMSKey-value store
Wide column store
Relational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.45
Rank#156  Overall
#15  Graph DBMS
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score104.92
Rank#11  Overall
#8  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitedgraph.iowww.esgyn.cncloud.google.com/­bigtablewww.microsoft.com/­en-us/­microsoft-365/­accessspark.apache.org/­sql
Technical documentationdgraph.io/­docscloud.google.com/­bigtable/­docsdeveloper.microsoft.com/­en-us/­accessspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDgraph Labs, Inc.EsgynGoogleMicrosoftApache Software Foundation
Initial release20162015201519922014
Current release1902 (16.0.11328.20222), March 20193.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialcommercial infoBundled with Microsoft OfficeOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC++, JavaC++Scala
Server operating systemsLinux
OS X
Windows
LinuxhostedWindows infoNot a real database server, but making use of DLLsLinux
OS X
Windows
Data schemeschema-freeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesnoyesyes
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 indexesyesyesnoyesno
SQL infoSupport of SQLnoyesnoyes infobut not compliant to any SQL standardSQL-like DML and DDL statements
APIs and other access methodsGraphQL query language
gRPC (using protocol buffers) API
HTTP API
ADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
ADO.NET
DAO
ODBC
OLE DB
JDBC
ODBC
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
All languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoJava Stored Proceduresnoyes infosince Access 2010 using the ACE-engineno
Triggersnononoyes infosince Access 2010 using the ACE-engineno
Partitioning methods infoMethods for storing different data on different nodesyesShardingShardingnoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSynchronous replication via RaftMulti-source replication between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zonesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)
Foreign keys infoReferential integritynoyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDAtomic single-row operationsACID infobut no files for transaction loggingno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infobut no files for transaction loggingyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlno infoPlanned for future releasesfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)no infoa simple user-level security was built in till version Access 2003no

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More resources
DgraphEsgynDBGoogle Cloud BigtableMicrosoft AccessSpark SQL
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