DBMS > Apache Drill vs. atoti vs. JaguarDB vs. Kinetica

System Properties Comparison Apache Drill vs. atoti vs. JaguarDB vs. Kinetica

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Performant, highly scalable DBMS for AI and IoT applications

Fully vectorized database across both GPUs and CPUs

Primary database model

Document store
Relational DBMS

Object oriented DBMS

Key-value store
Vector DBMS

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.90
Rank	#126	Overall
	#23	Document stores
	#60	Relational DBMS

Score	0.45
Rank	#253	Overall
	#13	Object oriented DBMS

Score	0.00
Rank	#385	Overall
	#61	Key-value stores
	#16	Vector DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Website

Technical documentation

drill.apache.org/docs

docs.atoti.io

www.jaguardb.com/support.html

docs.kinetica.com

Developer

Apache Software Foundation

ActiveViam

DataJaguar, Inc.

Kinetica

Initial release

2012

2015

2012

Current release

1.20.3, January 2023

3.3 July 2023

7.1, August 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free versions available

Open Source

GPL V3.0

commercial

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.

Implementation language

Java

C++

the server part. Clients available in other languages

C, C++

Server operating systems

Linux
OS X
Windows

Linux

Data scheme

schema-free

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

SQL SELECT statement is SQL:2003 compliant

Multidimensional Expressions (MDX)

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

Supported programming languages

C++

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

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

Python

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Depending on the underlying data source

yes

GPU vRAM or System RAM

User concepts

Access control

Depending on the underlying data source

rights management via user accounts

Access rights for users and roles on table level

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More resources

Recent citations in the news

Apache Drill case study: A tutorial on processing CSV files
9 June 2016, TheServerSide.com

Analyse Kafka messages with SQL queries using Apache Drill
27 September 2019, Towards Data Science

MapR Advances Support for Flexible and High Performance Analytics on JSON and S3 Data with Apache Drill
30 January 2019, businesswire.com

Apache Drill Adds New Data Formats
28 March 2022, iProgrammer

Apache Drill Eliminates ETL, Data Transformation for MapR Database
11 April 2016, The New Stack

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Kinetica ramps up RAG for generative AI, empowering enterprises with real-time operational data
18 March 2024, SiliconANGLE News

Kinetica: AI is a ‘killer app’ for data analytics
2 May 2023, Blocks & Files

Kinetica Taps Dell for Hardware
12 June 2018, Finovate

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