DBMS > Databend vs. Geode vs. Google BigQuery vs. Kinetica vs. TerarkDB

System Properties Comparison Databend vs. Geode vs. Google BigQuery vs. Kinetica vs. TerarkDB

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

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Large scale data warehouse service with append-only tables

Fully vectorized database across both GPUs and CPUs

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

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

geode.apache.org

cloud.google.com/bigquery

www.kinetica.com

github.com/bytedance/terarkdb

Technical documentation

docs.databend.com

geode.apache.org/docs

cloud.google.com/bigquery/docs

docs.kinetica.com

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Databend Labs

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Google

Kinetica

ByteDance, originally Terark

Initial release

2021

2002

2010

2012

2016

Current release

1.0.59, April 2023

1.1, February 2017

7.1, August 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

restricted open source version 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

Java

C, C++

C++

Server operating systems

hosted
Linux
macOS

All OS with a Java VM

the JDK (8 or later) is also required

hosted

Linux

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

SQL

Support of SQL

yes

SQL-like query language (OQL)

yes

SQL-like DML and DDL statements

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

Java Client API
Memcached protocol
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

C++ API
Java API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

.Net
All JVM based languages
C++
Groovy
Java
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

Triggers

yes

Cache Event Listeners

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

none

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

Source-replica replication

none

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

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

yes

yes, on a single node

Since BigQuery is designed for querying data

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

GPU vRAM or System RAM

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles

Access rights per client and object definable

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles on table level

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