DBMS > Apache Impala vs. Microsoft Access vs. Microsoft Azure AI Search vs. MySQL vs. Sequoiadb

System Properties Comparison Apache Impala vs. Microsoft Access vs. Microsoft Azure AI Search vs. MySQL vs. Sequoiadb

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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.)

Search-as-a-service for web and mobile app development

Widely used open source RDBMS

NewSQL database with distributed OLTP and SQL

Primary database model

Relational DBMS

Search engine

Relational DBMS

Key/Value like access via memcached API

Document store
Relational DBMS

Secondary database models

Document store

Vector DBMS

Document store
Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

impala.apache.org

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

azure.microsoft.com/en-us/services/search

www.mysql.com

www.sequoiadb.com

Technical documentation

impala.apache.org/impala-docs.html

developer.microsoft.com/en-us/access

learn.microsoft.com/en-us/azure/search

dev.mysql.com/doc

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Sequoiadb Ltd.

Initial release

2013

1992

2015

1995

2013

Current release

4.1.0, June 2022

1902 (16.0.11328.20222), March 2019

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Bundled with Microsoft Office

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Server: AGPL; Client: Apache V2

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

C and C++

C++

Server operating systems

Linux

Windows

Not a real database server, but making use of DLLs

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

JDBC
ODBC

ADO.NET
DAO
ODBC
OLE DB

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

proprietary protocol using JSON

Supported programming languages

All languages supporting JDBC/ODBC

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

C#
Java
JavaScript
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

since Access 2010 using the ACE-engine

yes

proprietary syntax

JavaScript

Triggers

yes

since Access 2010 using the ACE-engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

Source-replica 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

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

but no files for transaction logging

ACID

not for MyISAM storage engine

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

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

yes

using Azure authentication

Users with fine-grained authorization concept

no user groups or roles

simple password-based access control

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