DBMS > BigObject vs. IBM Db2 Event Store vs. Microsoft Access vs. Oracle vs. Vertica

System Properties Comparison BigObject vs. IBM Db2 Event Store vs. Microsoft Access vs. Oracle vs. Vertica

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Distributed Event Store optimized for Internet of Things use cases

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.)

Widely used RDBMS

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Event Store
Time Series DBMS

Relational DBMS

Column oriented

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

Spatial DBMS
Time Series DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Website

bigobject.io

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

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

www.oracle.com/database

www.vertica.com

Technical documentation

docs.bigobject.io

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

developer.microsoft.com/en-us/access

docs.oracle.com/en/database

vertica.com/documentation

Developer

BigObject, Inc.

IBM

Microsoft

Oracle

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2015

2017

1992

1980

2005

Current release

2.0

1902 (16.0.11328.20222), March 2019

23c, September 2023

12.0.3, January 2023

License

Commercial or Open Source

commercial

free community edition available

commercial

free developer edition available

commercial

Bundled with Microsoft Office

commercial

restricted free version is available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C and C++

C++

C and C++

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

Linux, macOS, Windows for the developer addition

Windows

Not a real database server, but making use of DLLs

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux

Data scheme

yes

Schemaless in JSON and XML columns

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL-like DML and DDL statements

yes

through the embedded Spark runtime

yes

but not compliant to any SQL standard

yes

with proprietary extensions

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

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

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

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

Server-side scripts

Stored procedures

Lua

yes

since Access 2010 using the ACE-engine

PL/SQL

also stored procedures in Java possible

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

since Access 2010 using the ACE-engine

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding, horizontal partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Active-active shard replication

none

Multi-source replication
Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

but no files for transaction logging

ACID

isolation level can be parameterized

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

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

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

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