DBMS > Apache Impala vs. ClickHouse vs. Datastax Enterprise vs. Microsoft Access vs. Riak KV

System Properties Comparison Apache Impala vs. ClickHouse vs. Datastax Enterprise vs. Microsoft Access vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

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.)

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	5.80
Rank	#60	Overall
	#4	Wide column stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

impala.apache.org

clickhouse.com

www.datastax.com/products/datastax-enterprise

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

Technical documentation

impala.apache.org/impala-docs.html

clickhouse.com/docs

docs.datastax.com

developer.microsoft.com/en-us/access

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Clickhouse Inc.

DataStax

Microsoft

OpenSource, formerly Basho Technologies

Initial release

2013

2016

2011

1992

2009

Current release

4.1.0, June 2022

v24.4.1.2088-stable, May 2024

6.8, April 2020

1902 (16.0.11328.20222), March 2019

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

Bundled with Microsoft Office

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

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Datastax Astra DB: Astra DB simplifies cloud-native Cassandra application development for your apps, microservices and functions. Deploy in minutes on AWS, Google Cloud, Azure, and have it managed for you by the experts, with serverless, pay-as-you-go pricing.

Implementation language

C++

Java

C++

Erlang

Server operating systems

Linux

FreeBSD
Linux
macOS

Linux
OS X

Windows

Not a real database server, but making use of DLLs

Linux
OS X

Data scheme

yes

schema-free

yes

schema-free

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

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like DML and DDL statements (CQL); Spark SQL

yes

but not compliant to any SQL standard

APIs and other access methods

JDBC
ODBC

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

ADO.NET
DAO
ODBC
OLE DB

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

since Access 2010 using the ACE-engine

Erlang

Triggers

yes

since Access 2010 using the ACE-engine

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

key based and custom

Sharding

no "single point of failure"

none

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

configurable replication factor, datacenter aware, advanced replication for edge computing

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

Atomicity and isolation are supported for single operations

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

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Access rights for users can be defined per object

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

yes, using Riak Security

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
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Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
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Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
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Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
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Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
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Licensing and pricing models

Annual subscription
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