DBMS > Databricks vs. OpenMLDB vs. Sequoiadb vs. Tkrzw vs. Yaacomo

System Properties Comparison Databricks vs. OpenMLDB vs. Sequoiadb vs. Tkrzw vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

An open-source machine learning database that provides a feature platform for training and inference

NewSQL database with distributed OLTP and SQL

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store
Relational DBMS

Time Series DBMS

Document store
Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Relational DBMS

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

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

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

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

yaacomo.com

Technical documentation

docs.databricks.com

openmldb.ai/docs/zh/main

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

Developer

Databricks

4 Paradigm Inc.

Sequoiadb Ltd.

Mikio Hirabayashi

Q2WEB GmbH

Initial release

2013

2020

2013

2020

2009

Current release

2024-2 February 2024

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache Version 2.0

commercial

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, Scala

C++

Server operating systems

hosted

Linux

Linux
macOS

Android
Linux
Windows

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

Fixed schema

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

with Databricks SQL

yes

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
SQLAlchemy

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

Python
R
Scala

C++
Go
Java
Python
Scala

.Net
C++
Java
PHP
Python

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Document is locked during a transaction

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

using specific database classes

yes

User concepts

Access control

fine grained access rights according to SQL-standard

simple password-based access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
» more

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2023
2 January 2024, Matthias Gelbmann, Paul Andlinger

show all

Recent citations in the news

Databricks is Taking the Ultimate Risk of Building 'USB for AI' – AIM
15 June 2024, Analytics India Magazine

The Three Big Announcements by Databricks AI Team in June 2024
17 June 2024, MarkTechPost

Databricks launches LakeFlow to help its customers build their data pipelines
12 June 2024, TechCrunch

Databricks tells investors annualized revenue will reach $2.4 billion at midway point of year
13 June 2024, CNBC

Databricks open-sources Unity Catalog, challenging Snowflake on interoperability for data workloads
12 June 2024, VentureBeat

provided by Google News

MLOp practice: using OpenMLDB in the real-time anti-fraud model for the bank's online transaction
23 August 2021, Towards Data Science

Predictive maintenance — 5minutes demo of an end to end machine learning project
13 August 2021, Towards Data Science

Compared to Native Spark 3.0, We Have Achieved Significant Optimization Effects in the AI
3 August 2021, Towards Data Science

provided by Google News

Share this page