DBMS > Atos Standard Common Repository vs. ClickHouse vs. InterSystems IRIS vs. Vertica vs. Weaviate

System Properties Comparison Atos Standard Common Repository vs. ClickHouse vs. InterSystems IRIS vs. Vertica vs. Weaviate

Editorial information provided by DB-Engines

Name

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

Description

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

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.

A containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalability

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.

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

Document store
Key-value store

Relational DBMS

Document store
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Column oriented

Vector DBMS

Secondary database models

Time Series DBMS

Spatial DBMS
Time Series DBMS

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

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	4.05
Rank	#83	Overall
	#14	Document stores
	#10	Key-value stores
	#1	Object oriented DBMS
	#45	Relational DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Score	1.73
Rank	#143	Overall
	#5	Vector DBMS

Website

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

clickhouse.com

www.intersystems.com/products/intersystems-iris

www.vertica.com

github.com/weaviate/weaviate
weaviate.io

Technical documentation

clickhouse.com/docs

docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls

vertica.com/documentation

weaviate.io/developers/weaviate

Developer

Atos Convergence Creators

Clickhouse Inc.

InterSystems

OpenText

previously Micro Focus and Hewlett Packard

Weaviate B.V.

Initial release

2016

2018

2005

2019

Current release

1703

v24.4.1.2088-stable, May 2024

2023.3, June 2023

12.0.3, January 2023

1.19, May 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Limited community edition free

Open Source

commercial license available with Weaviate Enterprise

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.

Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.

Implementation language

Java

C++

Server operating systems

Linux

FreeBSD
Linux
macOS

AIX
Linux
macOS
Ubuntu
Windows

Linux

Data scheme

Schema and schema-less with LDAP views

yes

depending on used data model

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

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

optional

yes

string, int, float, geo point, date, cross reference, fuzzy references

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.

yes

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

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

GraphQL is used as query language

APIs and other access methods

LDAP

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

JDBC
ODBC
RESTful HTTP API

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

character-based, interactive, front-end utility

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

All languages with LDAP bindings

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

.Net
C++
Java
JavaScript (Node.js)
Python

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

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

key based and custom

Sharding

horizontal partitioning, hierarchical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Source-replica replication

Multi-source replication

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

LDAP bind authentication

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.

yes

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

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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As a database supporting the development of generative AI and semantic search applications...
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Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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All companies that have data.
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Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
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