DBMS > dBASE vs. Google BigQuery vs. HugeGraph vs. IBM Db2 Event Store vs. Teradata

System Properties Comparison dBASE vs. Google BigQuery vs. HugeGraph vs. IBM Db2 Event Store vs. Teradata

Editorial information provided by DB-Engines

Name

Description

dBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.

Large scale data warehouse service with append-only tables

A fast-speed and highly-scalable Graph DBMS

Distributed Event Store optimized for Internet of Things use cases

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Graph DBMS

Event Store
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	10.34
Rank	#44	Overall
	#28	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

www.dbase.com

cloud.google.com/bigquery

github.com/hugegraph
hugegraph.apache.org

www.ibm.com/products/db2-event-store

www.teradata.com

Technical documentation

www.dbase.com/support/knowledgebase

cloud.google.com/bigquery/docs

hugegraph.apache.org/docs

www.ibm.com/docs/en/db2-event-store

docs.teradata.com

Developer

Asthon Tate

Google

Baidu

IBM

Teradata

Initial release

1979

2010

2018

2017

1984

Current release

dBASE 2019, 2019

0.9

2.0

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free developer edition available

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

Java

C and C++

Server operating systems

DOS

dBase Classic
Windows

dBase Pro

hosted

Linux
macOS
Unix

Linux

Linux, macOS, Windows for the developer addition

hosted
Linux

Data scheme

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

also supports composite index and range index

yes

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

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

SQL 2016 + extensions

APIs and other access methods

none

The IDE can access other DBMS or ODBC-sources.

RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

dBase proprietary IDE

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Groovy
Java
Python

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

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

Server-side scripts

Stored procedures

The IDE can access stored procedures in other database systems.

user defined functions

in JavaScript

asynchronous Gremlin script jobs

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

depending on used storage backend, e.g. Cassandra and HBase

Active-active shard replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

not for dBase internal data, but IDE does support transactions when accessing external DBMS

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

Access rights for users and roles

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

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions

fine grained access rights according to SQL-standard

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