DBMS > Databend vs. Google BigQuery vs. Machbase Neo vs. SpaceTime vs. Yaacomo

System Properties Comparison Databend vs. Google BigQuery vs. Machbase Neo vs. SpaceTime vs. Yaacomo

Editorial information provided by DB-Engines

Name

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

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

Large scale data warehouse service with append-only tables

TimeSeries DBMS for AIoT and BigData

SpaceTime is a spatio-temporal DBMS with a focus on performance.

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

Primary database model

Secondary database models

Relational DBMS

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

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	0.03
Rank	#392	Overall
	#8	Spatial DBMS

Website

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

cloud.google.com/bigquery

machbase.com

www.mireo.com/spacetime

yaacomo.com

Technical documentation

docs.databend.com

cloud.google.com/bigquery/docs

machbase.com/dbms

Developer

Databend Labs

Google

Machbase

Mireo

Q2WEB GmbH

Initial release

2021

2010

2013

2020

2009

Current release

1.0.59, April 2023

V8.0, August 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free test version available

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

Rust

C++

Server operating systems

hosted
Linux
macOS

hosted

Linux
macOS
Windows

Linux

Android
Linux
Windows

Data scheme

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

yes

SQL-like query language

A subset of ANSI SQL is implemented

yes

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

RESTful HTTP/JSON API

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

RESTful HTTP API

JDBC
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

C#
C++
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Fixed-grid hypercubes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

Real-time block device replication (DRBD)

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Since BigQuery is designed for querying data

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

volatile and lookup table

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles

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

Google Cloud Identity & Access Management (IAM)

simple password-based access control

yes

fine grained access rights according to SQL-standard

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