DBMS > Amazon DynamoDB vs. Google Cloud Bigtable vs. Graphite vs. HugeGraph vs. Teradata

System Properties Comparison Amazon DynamoDB vs. Google Cloud Bigtable vs. Graphite vs. HugeGraph vs. Teradata

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

A fast-speed and highly-scalable Graph DBMS

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Document store
Key-value store

Key-value store
Wide column store

Time Series DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	4.57
Rank	#73	Overall
	#5	Time Series DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

aws.amazon.com/dynamodb

cloud.google.com/bigtable

github.com/graphite-project/graphite-web

github.com/hugegraph
hugegraph.apache.org

www.teradata.com

Technical documentation

docs.aws.amazon.com/dynamodb

cloud.google.com/bigtable/docs

graphite.readthedocs.io

hugegraph.apache.org/docs

docs.teradata.com

Developer

Amazon

Google

Chris Davis

Baidu

Teradata

Initial release

2012

2015

2006

2018

1984

Current release

0.9

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

Python

Java

Server operating systems

hosted

Linux
Unix

Linux
macOS
Unix

hosted
Linux

Data scheme

schema-free

yes

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

yes

also supports composite index and range index

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

SQL 2016 + extensions

APIs and other access methods

RESTful HTTP API

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

HTTP API
Sockets

Java API
RESTful HTTP API
TinkerPop Gremlin

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

JavaScript (Node.js)
Python

Groovy
Java
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

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

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

yes

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

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

none

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

ACID

ACID across one or more tables within a single AWS account and region

Atomic single-row operations

ACID

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

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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

Users, roles and permissions

fine grained access rights according to SQL-standard

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