DBMS > Google BigQuery vs. Graph Engine vs. Graphite vs. HEAVY.AI vs. Snowflake

System Properties Comparison Google BigQuery vs. Graph Engine vs. Graphite vs. HEAVY.AI vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Time Series DBMS

Relational DBMS

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	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	4.57
Rank	#73	Overall
	#5	Time Series DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

cloud.google.com/bigquery

www.graphengine.io

github.com/graphite-project/graphite-web

github.com/heavyai/heavydb
www.heavy.ai

www.snowflake.com

Technical documentation

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

graphite.readthedocs.io

docs.heavy.ai

docs.snowflake.net/manuals/index.html

Developer

Google

Microsoft

Chris Davis

HEAVY.AI, Inc.

Snowflake Computing Inc.

Initial release

2010

2006

2016

2014

Current release

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition 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

.NET and C

Python

C++ and CUDA

Server operating systems

hosted

.NET

Linux
Unix

Linux

hosted

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

Numeric data only

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

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

HTTP API
Sockets

JDBC
ODBC
Thrift
Vega

CLI Client
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

JavaScript (Node.js)
Python

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

none

Sharding

Round robin

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

locking

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

In-memory capabilities

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

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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