DBMS > EsgynDB vs. Google Cloud Bigtable vs. Microsoft Access vs. OpenEdge vs. Snowflake

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. Microsoft Access vs. OpenEdge vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Google'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.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Application development environment with integrated database management system

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

Key-value store
Wide column store

Relational DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.esgyn.cn

cloud.google.com/bigtable

www.microsoft.com/en-us/microsoft-365/access

www.progress.com/openedge

www.snowflake.com

Technical documentation

cloud.google.com/bigtable/docs

developer.microsoft.com/en-us/access

documentation.progress.com/output/ua/OpenEdge_latest

docs.snowflake.net/manuals/index.html

Developer

Esgyn

Google

Microsoft

Progress Software Corporation

Snowflake Computing Inc.

Initial release

2015

1992

1984

2014

Current release

1902 (16.0.11328.20222), March 2019

OpenEdge 12.2, March 2020

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

C++

Server operating systems

Linux

hosted

Windows

Not a real database server, but making use of DLLs

AIX
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

yes

close to SQL 92

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

ADO.NET
DAO
ODBC
OLE DB

JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C#
C++
Go
Java
JavaScript (Node.js)
Python

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

Progress proprietary ABL (Advanced Business Language)

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Java Stored Procedures

yes

since Access 2010 using the ACE-engine

yes

user defined functions

Triggers

yes

since Access 2010 using the ACE-engine

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

since Version 11.4

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Internal replication in Colossus, and regional replication between two clusters in different zones

none

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Atomic single-row operations

ACID

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

a simple user-level security was built in till version Access 2003

Users and groups

Users with fine-grained authorization concept, user roles and pluggable authentication

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