DBMS > Google BigQuery vs. Graphite vs. Machbase Neo vs. Microsoft Azure Cosmos DB vs. STSdb

System Properties Comparison Google BigQuery vs. Graphite vs. Machbase Neo vs. Microsoft Azure Cosmos DB vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

TimeSeries DBMS for AIoT and BigData

Globally distributed, horizontally scalable, multi-model database service

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Secondary database models

Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.57
Rank	#73	Overall
	#5	Time Series DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

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

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

cloud.google.com/bigquery

github.com/graphite-project/graphite-web

machbase.com

azure.microsoft.com/services/cosmos-db

github.com/STSSoft/STSdb4

Technical documentation

cloud.google.com/bigquery/docs

graphite.readthedocs.io

machbase.com/dbms

learn.microsoft.com/azure/cosmos-db

Developer

Google

Chris Davis

Machbase

Microsoft

STS Soft SC

Initial release

2010

2006

2013

2014

2011

Current release

V8.0, August 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

free test version available

commercial

Open Source

GPLv2, 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

Python

Server operating systems

hosted

Linux
Unix

Linux
macOS
Windows

hosted

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

yes

JSON types

yes

primitive types and user defined types (classes)

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 query language

APIs and other access methods

RESTful HTTP/JSON API

HTTP API
Sockets

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

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

.NET Client API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript (Node.js)
Python

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

C#
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

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

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

locking

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

simple password-based access control

Access rights can be defined down to the item level

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