DBMS > Apache Impala vs. Atos Standard Common Repository vs. Google BigQuery vs. Microsoft Access vs. VictoriaMetrics

System Properties Comparison Apache Impala vs. Atos Standard Common Repository vs. Google BigQuery vs. Microsoft Access vs. VictoriaMetrics

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Large scale data warehouse service with append-only tables

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 fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Relational DBMS

Document store
Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

impala.apache.org

atos.net/en/convergence-creators/portfolio/standard-common-repository

cloud.google.com/bigquery

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

victoriametrics.com

Technical documentation

impala.apache.org/impala-docs.html

cloud.google.com/bigquery/docs

developer.microsoft.com/en-us/access

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Atos Convergence Creators

Google

Microsoft

VictoriaMetrics

Initial release

2013

2016

2010

1992

2018

Current release

4.1.0, June 2022

1703

1902 (16.0.11328.20222), March 2019

v1.91, May 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Bundled with Microsoft Office

Open Source

Apache Version 2.0

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

FreeBSD
Linux
macOS
OpenBSD

Data scheme

yes

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

yes

optional

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

SQL-like DML and DDL statements

yes

but not compliant to any SQL standard

APIs and other access methods

JDBC
ODBC

LDAP

RESTful HTTP/JSON API

ADO.NET
DAO
ODBC
OLE DB

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

yes

since Access 2010 using the ACE-engine

Triggers

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

none

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

Since BigQuery is designed for querying data

ACID

but no files for transaction logging

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

LDAP bind authentication

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.