DBMS > EsgynDB vs. Google BigQuery vs. Hawkular Metrics vs. Microsoft Access vs. Teradata

System Properties Comparison EsgynDB vs. Google BigQuery vs. Hawkular Metrics vs. Microsoft Access vs. Teradata

Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

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.)

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

www.esgyn.cn

cloud.google.com/bigquery

www.hawkular.org

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

www.teradata.com

Technical documentation

cloud.google.com/bigquery/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

developer.microsoft.com/en-us/access

docs.teradata.com

Developer

Esgyn

Google

Community supported by Red Hat

Microsoft

Teradata

Initial release

2015

2010

2014

1992

1984

Current release

1902 (16.0.11328.20222), March 2019

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

Java

C++

Server operating systems

Linux

hosted

Linux
OS X
Windows

Windows

Not a real database server, but making use of DLLs

hosted
Linux

Data scheme

yes

schema-free

yes

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

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

but not compliant to any SQL standard

yes

SQL 2016 + extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

HTTP REST

ADO.NET
DAO
ODBC
OLE DB

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
Python
Ruby

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

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

yes

since Access 2010 using the ACE-engine

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

via Hawkular Alerting

yes

since Access 2010 using the ACE-engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on Cassandra

none

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

selectable replication factor

based on Cassandra

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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

fine grained access rights according to SQL-standard

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