DBMS > Databend vs. Kinetica vs. LevelDB vs. Microsoft Azure Cosmos DB vs. WakandaDB

System Properties Comparison Databend vs. Kinetica vs. LevelDB vs. Microsoft Azure Cosmos DB vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Fully vectorized database across both GPUs and CPUs

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Globally distributed, horizontally scalable, multi-model database service

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Relational DBMS

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Object oriented DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

github.com/datafuselabs/databend
www.databend.com

www.kinetica.com

github.com/google/leveldb

azure.microsoft.com/services/cosmos-db

wakanda.github.io

Technical documentation

docs.databend.com

docs.kinetica.com

github.com/google/leveldb/blob/main/doc/index.md

learn.microsoft.com/azure/cosmos-db

wakanda.github.io/doc

Developer

Databend Labs

Kinetica

Google

Microsoft

Wakanda SAS

Initial release

2021

2012

2011

2014

2012

Current release

1.0.59, April 2023

7.1, August 2021

1.23, February 2021

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

BSD

commercial

Open Source

AGPLv3, extended commercial license available

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

Rust

C, C++

C++

C++, JavaScript

Server operating systems

hosted
Linux
macOS

Linux

Illumos
Linux
OS X
Windows

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

RESTful HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

C++
Java
JavaScript (Node.js)
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

JavaScript

Server-side scripts

Stored procedures

user defined functions

JavaScript

yes

Triggers

yes

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

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

none

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

User concepts

Access control

Users with fine-grained authorization concept, user roles

Access rights for users and roles on table level

Access rights can be defined down to the item level

yes

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