DBMS > Google BigQuery vs. IBM Db2 Event Store vs. Microsoft Access vs. Realm

System Properties Comparison Google BigQuery vs. IBM Db2 Event Store vs. Microsoft Access vs. Realm

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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.)

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Relational DBMS

Document store

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.23
Rank	#316	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	105.40
Rank	#11	Overall
	#8	Relational DBMS

Score	7.71
Rank	#52	Overall
	#9	Document stores

Website

cloud.google.com/bigquery

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

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

realm.io

Technical documentation

cloud.google.com/bigquery/docs

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

developer.microsoft.com/en-us/access

realm.io/docs

Developer

Google

IBM

Microsoft

Realm, acquired by MongoDB in May 2019

Initial release

2010

2017

1992

2014

Current release

2.0

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

commercial

free developer edition available

commercial

Bundled with Microsoft Office

Open Source

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 and C++

C++

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Windows

Not a real database server, but making use of DLLs

Android
Backend: server-less
iOS
Windows

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.

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

but not compliant to any SQL standard

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

since Access 2010 using the ACE-engine

runs within the applications so server-side scripts are unnecessary

Triggers

yes

since Access 2010 using the ACE-engine

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

but no files for transaction logging

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

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

In-Memory realm

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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

yes

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