DBMS > Google Cloud Bigtable vs. HEAVY.AI vs. HugeGraph vs. MarkLogic vs. Snowflake

System Properties Comparison Google Cloud Bigtable vs. HEAVY.AI vs. HugeGraph vs. MarkLogic vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

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

A fast-speed and highly-scalable Graph DBMS

Operational and transactional Enterprise NoSQL database

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

Primary database model

Key-value store
Wide column store

Relational DBMS

Graph DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

cloud.google.com/bigtable

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

github.com/hugegraph
hugegraph.apache.org

www.marklogic.com

www.snowflake.com

Technical documentation

cloud.google.com/bigtable/docs

docs.heavy.ai

hugegraph.apache.org/docs

docs.marklogic.com

docs.snowflake.net/manuals/index.html

Developer

Google

HEAVY.AI, Inc.

Baidu

MarkLogic Corp.

Snowflake Computing Inc.

Initial release

2015

2016

2018

2001

2014

Current release

5.10, January 2022

0.9

11.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2.0

commercial

restricted free version is 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

C++ and CUDA

Java

C++

Server operating systems

hosted

Linux

Linux
macOS
Unix

Linux
OS X
Windows

hosted

Data scheme

schema-free

yes

schema-free

Schema can be enforced

yes

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

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

SQL92

yes

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JDBC
ODBC
Thrift
Vega

Java API
RESTful HTTP API
TinkerPop Gremlin

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

CLI Client
JDBC
ODBC

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
Python

All languages supporting JDBC/ODBC/Thrift
Python

Groovy
Java
Python

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

via XQuery or JavaScript

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

Atomic single-row operations

ACID

can act as a resource manager in an XA/JTA transaction

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

yes, with Range Indexes

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

Users, roles and permissions

Role-based access control at the document and subdocument levels

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

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